This document discusses approaches to embedding performance testing within an agile software development model. It proposes shifting performance testing earlier in the development process ("shift left") through feature branch testing and automation. Automating performance tests within a continuous integration/continuous deployment pipeline can find issues sooner and speed delivery. Challenges include incomplete integration testing at the feature level and engagement between performance and development teams. The results of a proof of concept automating performance testing in a pipeline are presented.
Big Data Analytics on Customer Behaviors with Kinect Sensor NetworkCSCJournals
In modern enterprises, customer data is valuable for identifying their behavioral patterns and developing marketing strategies that can align with the preferences of different customers. The objective of this research is to develop a framework that promotes the use of Kinect sensors for Big Data Analytics on customer behavior analysis. Kinect enables 3D motion capture, facial recognition and voice recognition capabilities which allow to analyze customer behaviors in various aspects. Information fusion on the network of multiple Kinect sensors can achieve enhanced insight of the customer emotion, habits and consuming tendencies. Big Data Analytic techniques such as clustering and visualization are applied on the data collected from the sensors to provide better comprehension on the customers. Prediction on how to improve the customer relationship can be made to stimulate the vendition. Finally, an experimental system is designed based on the proposed framework as an illustration of the framework implementation.
Activity-Based Costing in Healthcare During COVID-19: Meeting Four Critical N...Health Catalyst
As health systems increasingly transition to a value-based care model, the financial strains and uncertainty of COVID-19 have placed more urgency on cost management. More than ever, organizations need a costing solution that helps them understand the true value of their services. With the right next-generation activity-based costing (ABC) tool, health systems can access the detailed data they need to lower the cost of care, automate costing activities, and reduce administrative costs while preparing for the mounting intricacy of the post-pandemic setting.
Activity-based costing meets healthcare’s complex COVID-19-era costing needs by addressing four big challenges:
Data management.
Scalability.
Ongoing maintenance.
Adoption.
Why Health Systems Must Use Data Science to Improve OutcomesHealth Catalyst
The document discusses how a large health system used data science to improve patient outcomes and reduce costs related to orthopedic surgery readmissions. By using logistic regression models, the health system found that factors like BMI were not actually associated with readmissions. This allowed them to avoid unnecessary pre-op interventions and instead focus on other factors identified by the models, like behavior disorders and opioid use, that were tied to readmissions. Data science helped the health system optimize their improvement processes and resources to achieve the desired outcomes of reducing readmissions.
Network, Technology, and Data: Missing Pieces of the Puzzle for Clinical Tria...Health Catalyst
There is a massive shortfall in the enrollment and accrual of patients for clinical trials. Identifying the “right patients for the right trials at the right time” is a growing concern for providers, pharmaceutical companies, and clinical research organizations. In this webinar, we will discuss the evolution of clinical trials, including how to break barriers to enable successful clinical research as a care option, how clinical research impacts patient satisfaction and revenue, and more.
Tackle These 8 Challenges of MACRA Quality MeasuresHealth Catalyst
The Medicare Access and CHIP Reauthorization Act (MACRA) appears to be a reporting challenge for many healthcare provider systems with few resources for managing the menagerie of measures. Indeed, with more than 270 measures in play, many systems have yet to jump in, but the deadline is inevitable. A plan of action is possible by recognizing and acting on these eight challenge areas:
Challenge #1: High-level performance insight
Challenge #2: Defining measure specifications
Challenge #3: Data quality reporting requirements
Challenge #4: Benchmarking data
Challenge #5: Proactively increasing measures surveillance to enhance outcomes
Challenge #6: Strategically aligning measures on which to base risk
Challenge #7: Identifying measures with the largest financial impact
Challenge #8: Taking risk in multi-year, value-based contracts
Mid-to-large size provider groups need a strategy around MACRA quality measures and a tool to help them make sense of all the reporting requirements.
Cloud computing promises to fundamentally transform the global healthcare industry. But most healthcare providers have only just started to understand the power of cloud to not only drive efficiency, but also to redefine collaboration, partnering, and business models. The IBM Institute for Business Value point-of-view explores the opportunities and implications of cloud computing to help global healthcare companies meet new competitive pressures and ever-expanding consumer expectations.
Analytics is a key enabler for life sciences and healthcare organizations to create better outcomes for patients, customers and other stakeholders across the entire healthcare ecosystem. While almost two-thirds of organizations across the healthcare ecosystem have analytics strategies in place, our research shows that only a fifth are driving analytics adoption across the enterprise. The key barriers are a lack of data management capabilities and skilled analysts, as well as poor organizational change management. To develop and translate insights into actions that enhance outcomes, organizations will need to collaborate across an expanding ecosystem.
Your cognitive future: How next-gen computing changes the way we live and workIBM in Healthcare
The healthcare industry is undergoing significant change driven by six disruptive forces - rapid digitization, changing consumer expectations, regulatory complexities, increasing healthcare demand, shortage of skilled resources and elevating healthcare costs. To meet the implication of these forces, healthcare organizations must excel in engaging with consumers, discovering new ideas and taking effective decisions
Currently, traditional analytics capabilities are unable to exploit maximum value from the ever increasing data resource constraining organization’s achievements and performance. But cognitive computing has the ability to bridge this gap and can open up fresh opportunities for the healthcare industry. It is already helping healthcare organizations to provide personalized care, effective decisions and more innovative solutions.
Big Data Analytics on Customer Behaviors with Kinect Sensor NetworkCSCJournals
In modern enterprises, customer data is valuable for identifying their behavioral patterns and developing marketing strategies that can align with the preferences of different customers. The objective of this research is to develop a framework that promotes the use of Kinect sensors for Big Data Analytics on customer behavior analysis. Kinect enables 3D motion capture, facial recognition and voice recognition capabilities which allow to analyze customer behaviors in various aspects. Information fusion on the network of multiple Kinect sensors can achieve enhanced insight of the customer emotion, habits and consuming tendencies. Big Data Analytic techniques such as clustering and visualization are applied on the data collected from the sensors to provide better comprehension on the customers. Prediction on how to improve the customer relationship can be made to stimulate the vendition. Finally, an experimental system is designed based on the proposed framework as an illustration of the framework implementation.
Activity-Based Costing in Healthcare During COVID-19: Meeting Four Critical N...Health Catalyst
As health systems increasingly transition to a value-based care model, the financial strains and uncertainty of COVID-19 have placed more urgency on cost management. More than ever, organizations need a costing solution that helps them understand the true value of their services. With the right next-generation activity-based costing (ABC) tool, health systems can access the detailed data they need to lower the cost of care, automate costing activities, and reduce administrative costs while preparing for the mounting intricacy of the post-pandemic setting.
Activity-based costing meets healthcare’s complex COVID-19-era costing needs by addressing four big challenges:
Data management.
Scalability.
Ongoing maintenance.
Adoption.
Why Health Systems Must Use Data Science to Improve OutcomesHealth Catalyst
The document discusses how a large health system used data science to improve patient outcomes and reduce costs related to orthopedic surgery readmissions. By using logistic regression models, the health system found that factors like BMI were not actually associated with readmissions. This allowed them to avoid unnecessary pre-op interventions and instead focus on other factors identified by the models, like behavior disorders and opioid use, that were tied to readmissions. Data science helped the health system optimize their improvement processes and resources to achieve the desired outcomes of reducing readmissions.
Network, Technology, and Data: Missing Pieces of the Puzzle for Clinical Tria...Health Catalyst
There is a massive shortfall in the enrollment and accrual of patients for clinical trials. Identifying the “right patients for the right trials at the right time” is a growing concern for providers, pharmaceutical companies, and clinical research organizations. In this webinar, we will discuss the evolution of clinical trials, including how to break barriers to enable successful clinical research as a care option, how clinical research impacts patient satisfaction and revenue, and more.
Tackle These 8 Challenges of MACRA Quality MeasuresHealth Catalyst
The Medicare Access and CHIP Reauthorization Act (MACRA) appears to be a reporting challenge for many healthcare provider systems with few resources for managing the menagerie of measures. Indeed, with more than 270 measures in play, many systems have yet to jump in, but the deadline is inevitable. A plan of action is possible by recognizing and acting on these eight challenge areas:
Challenge #1: High-level performance insight
Challenge #2: Defining measure specifications
Challenge #3: Data quality reporting requirements
Challenge #4: Benchmarking data
Challenge #5: Proactively increasing measures surveillance to enhance outcomes
Challenge #6: Strategically aligning measures on which to base risk
Challenge #7: Identifying measures with the largest financial impact
Challenge #8: Taking risk in multi-year, value-based contracts
Mid-to-large size provider groups need a strategy around MACRA quality measures and a tool to help them make sense of all the reporting requirements.
Cloud computing promises to fundamentally transform the global healthcare industry. But most healthcare providers have only just started to understand the power of cloud to not only drive efficiency, but also to redefine collaboration, partnering, and business models. The IBM Institute for Business Value point-of-view explores the opportunities and implications of cloud computing to help global healthcare companies meet new competitive pressures and ever-expanding consumer expectations.
Analytics is a key enabler for life sciences and healthcare organizations to create better outcomes for patients, customers and other stakeholders across the entire healthcare ecosystem. While almost two-thirds of organizations across the healthcare ecosystem have analytics strategies in place, our research shows that only a fifth are driving analytics adoption across the enterprise. The key barriers are a lack of data management capabilities and skilled analysts, as well as poor organizational change management. To develop and translate insights into actions that enhance outcomes, organizations will need to collaborate across an expanding ecosystem.
Your cognitive future: How next-gen computing changes the way we live and workIBM in Healthcare
The healthcare industry is undergoing significant change driven by six disruptive forces - rapid digitization, changing consumer expectations, regulatory complexities, increasing healthcare demand, shortage of skilled resources and elevating healthcare costs. To meet the implication of these forces, healthcare organizations must excel in engaging with consumers, discovering new ideas and taking effective decisions
Currently, traditional analytics capabilities are unable to exploit maximum value from the ever increasing data resource constraining organization’s achievements and performance. But cognitive computing has the ability to bridge this gap and can open up fresh opportunities for the healthcare industry. It is already helping healthcare organizations to provide personalized care, effective decisions and more innovative solutions.
The healthcare transformation from fee for service to fee for outcomes just got an adrenaline shot in the arm April 27th when the Department of Health and Human Services surprised many in the market by announcing a Quality Payment Program, a proposed set of new rules to take effect in 2019 based on key provisions of the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA).
Network Optimization: Why Physician Quality Should Drive Your Benefits StrategyGrand Rounds
Employers and payers are increasingly interested in narrow network or "high performance" networks to control healthcare costs. But there's a science to reshaping your physician network to cut costs while avoiding member blowback. Learn how to optimize networks for cost and quality, while reassuring your employees that they can still access the care they need.
Reinventing Life Sciences: How emerging ecosystems fuel innovationIBM in Healthcare
Persistent disruptive forces in life sciences now threaten traditional business models over the medium to long term. While high rates of return and strong performance may have masked these forces in the past, today they must be recognized and addressed. Organizations need new ways to continue to thrive despite such hurdles.
This latest research study by IBM Instritute of Business Value in collaboration with the University of California, San Diego and Oxford Economics, led to a target innovation model that can guide organizations to discover operational efficiencies, nurture new growth and get positioned more strategically in the new life sciences and healthcare ecosystem.
Against the Odds: How this Small Community Hospital Used Six Strategies to Su...Health Catalyst
The constant thread weaving through every healthcare organizational strategy should be adherence to the Triple Aim. But with uncertainty generated by the changes at the federal level, healthcare organizations may be tempted to put their value-based care plans on hold. This article explains why that’s not necessary and lists six strategies for thriving under a fee-for-value model: 1.) Use Leadership and Team Structure to Support Improvement 2.) Drive Down Costs 3.) Reduce Unnecessary Waste 4.) Encourage the Learning Organization 5.) Prioritize Patient Education 6.) Track Data and Outcomes This blog cites one small medical center with odds stacked against it, and how it is managing to not only weather the changes, but also distinguish itself by staying true to the values of the Triple Aim.
Reviewing the Healthcare Analytics Adoption Model: A Roadmap and Recipe for A...Health Catalyst
Dale Sanders provides an update on the Healthcare Analytics Adoption Model. Dale published the first version of this model in 2002, calling it the Analytics Capability Maturity Model. The three intentions at that time are the same as they are today: 1) Provide healthcare leaders with a clear roadmap for the progression of analytic maturity in their organization. 2) Provide vendors with a roadmap to meet the analytic needs of clients. 3) Create a common framework to benchmark the progressive adoption of analytics at the industry level.
In 2012, Dale co-published a new version of the Model with Dr. Denis Protti, rebranding it the Healthcare Analytics Adoption Model and purposely borrowing from the widespread adoption of the EMR Adoption Model (EMRAM) published and supported by HIMSS. In 2015, Dale transferred the model under a creative commons copyright to HIMSS to create a vendor-independent industry standard that is now widely applied to support the original three intentions. He continues to collaborate with HIMSS to progress the Model.
During this webinar, Dale:
-Reviews the current state of the Health Catalyst Model, including recent changes that advocate a ninth level—direct-to-patient analytics and AI.
-Shares his observations of maturity in the market.
-Provides an update on the current state of the HIMSS Adoption Model for Analytic Maturity.
Cloud computing promises to fundamentally transform the global life sciences industry. But most life sciences organizations have only just started to understand the power of cloud to not only drive efficiency, but also to redefine collaboration, partnering, and business models.
Life sciences organizations are hungry for the capabilities that cloud can deliver, to meet new competitive pressures and ever-expanding consumer expectations.
This new IBM Institute for Business Value (IBV) Cloud point-of-view (POV) for the life sciences industry explores the opportunities and implications of cloud computing for global life sciences companies. It provides a roadmap to formulate and execute cloud strategies.
Hospital Value-Based Purchasing: Leveraging Analytics for HVBP Prospective Pa...Perficient, Inc.
This document provides an overview of the Hospital Value-Based Purchasing (HVBP) program, including the timeline, measures, and calculation methodology. It discusses how HVBP rewards hospitals with incentive payments based on performance on clinical process, patient experience, outcome, and efficiency measures. The presentation agenda outlines covering HVBP overview, calculation, a demonstration of an analytics application, and Q&A. Eligible hospitals are acute care hospitals paid under the Inpatient Prospective Payment System, excluding those with deficiencies posing immediate jeopardy to patient health and safety.
The evolution of life science ecosystems: Five effective innovation approache...IBM in Healthcare
The life sciences industry, like many others, faces broad disruption and challenges on fronts ranging from technology to regulation to product resourcing. Traditionally, innovation has been a key driver of success for life sciences organizations, and it will continue to play a critical role for an industry that seeks to sustain this momentum.This report, the third of the Innovating Life Sciences series, identifies five strategies that differentiate the more successful academic life sciences institutions from the rest.
The Doctor’s Orders for Engaging Physicians to Drive ImprovementsHealth Catalyst
Physicians drive the majority of all quality and cost decisions, yet reimbursement pressures, competing time pressures, misaligned incentives, and a lack of credible data often make engaging clinicians in improvement work one of the biggest challenges in healthcare.
David Wild, MD, MBA, and Jack Beal, JD, explore how to spread data to the edges of the organization and engage physicians in leading a continuum of improvement across an entire organization.
During this webinar, our presenters:
• Identify the levels of physician leadership in your organization you can engage to drive improvement.
• Pinpoint the types of data and information of most interest to physician leaders.
• Propose several ways data to use data to engage physicians in leading improvement work.
• Help you develop at least one mechanism you can use to better engage physicians in improvement work at your organization.
How to Use Data to Improve Patient Safety: Part 2Health Catalyst
Stan and Valere will discuss how using an automated trigger tool for all-cause harm reviews will provide timely, real-time patient safety data useful to drive down harm rates with earlier interventions. Additional benefits of this approach include having a more accurate and robust source of data for identifying harm trends to then be able to integrate the findings into existing quality improvement processes for further quality improvement efforts.
Attendees will learn how to:
Understand the importance of dedicating resources to impact downstream costs
Identify their key sources of Patient Safety data
Integrate Patient Safety data in to existing Quality Improvement Processes
Learn and improve from real-time safety analytics combined with a Culture of Safety
The Top Five Essentials for Quality Improvement in HealthcareHealth Catalyst
Quality improvement in healthcare is complicated, but we’re beginning to understand what successful quality improvement programs have in common:
Adaptive leadership, culture, and governance
Analytics
Evidence- and consensus-based best practices
Adoption
Financial alignment
Although understanding the top five essentials for quality improvement in healthcare is key, it’s equally important to understand the most useful definitions and key considerations. For example, how different service delivery models (telemedicine, ACO, etc.) impact quality improvement programs and how quality improvement starts with an organization’s underlying systems of care.
This executive report takes an in-depth look at quality improvement with the goal of providing health systems with not only the top five essentials but also a more comprehensive understanding of the topic so they’re in a better position to improve quality and, ultimately, transform healthcare.
Go deeper with athenahealth specialists to discover all that you need to know and some things you may not know about Meaningful Use Stage 2 and the newest government updates.
How to Eliminate the Burden of Provider Quality Measurement: Able HealthHealth Catalyst
Quality measurement is complicated by incomplete data, calculations, visualizations, and workflows. As a result, quality measurement is a significant burden for medical groups. In fact, research that Health Affairs published in 2016 quantified the burden as 785 hours per provider per year.
That's why Health Catalyst is excited to introduce Able Health, the only quality measures solution that’s truly complete.
In this webinar, you’ll learn how Able Health combines all data, measures, visualizations, and workflows (monitor, improve, and submit) into one complete solution. Eliminating the complexity, and therefore the burden, of provider quality measurement means you spend more time improving performance and less time managing data.
You’ll also learn how each of the three core components of the Able Health solution makes more efficient quality measurement possible:
-Measures engine—calculates performance for all provider quality measures for all payer programs using every available data element.
-Performance dashboard—visualizes all performance metrics for daily tracking, prioritization, and internal reporting for all stakeholders, especially physicians.
-Submission engine—submits compliant data to payers.
SXSW Panel Picker slide show for Agile Development and the FDA
Developing software for the healthcare sector is difficult enough, but doing so under the scrutiny of the FDA can seem impossible. However, if you want to have an impact at the point of patient care the FDA is going to be a factor in your development. We will look at ways to marry the seemingly contradictory philosophies of Agile development, with its high efficiency, low documentation process, with the FDA regulated requirements of complete audibility and seemingly endless piles of paperwork. Through a real-life case study we will looks at modern software development practices through the lens of the FDA.
Apervita received Frost & Sullivan's 2015 New Product Innovation Award for its secure, self-service analytics platform that allows healthcare organizations to easily publish, access, and commercialize clinical decision support rules, quality measures, and other analytics. The platform addresses the growing need for affordable, customizable analytics solutions. Apervita received high scores in Frost & Sullivan's evaluation for its strong match to customer needs, ease of use, and ability to empower sharing of best practices.
Business Intelligence And Healthcare White Paper (English)smitchell1974
Major applications of business intelligence software in the healthcare industry include:
1) Financial analysis to reduce costs and ensure quality care
2) Quality performance and safety analysis to improve clinical processes and outcomes
3) Marketing analysis to better target performance goals and identify ways to improve care
Strategic Options for Analytics in HealthcareDale Sanders
There are essentially four analytic strategies available in the healthcare IT market at present. This slide summarizes those options, the pros and cons, and vendors in the space.
This document discusses acceptance test driven development (ATDD). It describes how ATDD involves first writing acceptance tests based on requirements before writing unit tests or code. This ensures requirements are clearly understood and tests provide feedback during development. ATDD tools like Concordion and FitNesse are mentioned for automating acceptance tests in a readable format. Benefits of ATDD include improved requirements understanding, early detection of failures, and reduced defects through continuous feedback.
In many web or cloud applications, performance testing is critical part of application testing since it affects
business revenue, credibility, and customer satisfaction. Conventional software development models are known
to pushing the performance testing to the very end of project, with the expectations that, only minor tweaks
and tune up are required to meet the performance requirements from the business, however any major
performance bottlenecks found during this phase were major factors for delay in Go to Market. With more and
more companies are adapting the agile software development process which believes in performance testing
should never be an afterthought but it should tightly integrate from initial planning to production analysis of
software development lifecycle. This white paper explains how any company can integrate performance testing
into agile process, and key barriers for agile performance testing when team decides to adopt agile performance
testing.
The healthcare transformation from fee for service to fee for outcomes just got an adrenaline shot in the arm April 27th when the Department of Health and Human Services surprised many in the market by announcing a Quality Payment Program, a proposed set of new rules to take effect in 2019 based on key provisions of the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA).
Network Optimization: Why Physician Quality Should Drive Your Benefits StrategyGrand Rounds
Employers and payers are increasingly interested in narrow network or "high performance" networks to control healthcare costs. But there's a science to reshaping your physician network to cut costs while avoiding member blowback. Learn how to optimize networks for cost and quality, while reassuring your employees that they can still access the care they need.
Reinventing Life Sciences: How emerging ecosystems fuel innovationIBM in Healthcare
Persistent disruptive forces in life sciences now threaten traditional business models over the medium to long term. While high rates of return and strong performance may have masked these forces in the past, today they must be recognized and addressed. Organizations need new ways to continue to thrive despite such hurdles.
This latest research study by IBM Instritute of Business Value in collaboration with the University of California, San Diego and Oxford Economics, led to a target innovation model that can guide organizations to discover operational efficiencies, nurture new growth and get positioned more strategically in the new life sciences and healthcare ecosystem.
Against the Odds: How this Small Community Hospital Used Six Strategies to Su...Health Catalyst
The constant thread weaving through every healthcare organizational strategy should be adherence to the Triple Aim. But with uncertainty generated by the changes at the federal level, healthcare organizations may be tempted to put their value-based care plans on hold. This article explains why that’s not necessary and lists six strategies for thriving under a fee-for-value model: 1.) Use Leadership and Team Structure to Support Improvement 2.) Drive Down Costs 3.) Reduce Unnecessary Waste 4.) Encourage the Learning Organization 5.) Prioritize Patient Education 6.) Track Data and Outcomes This blog cites one small medical center with odds stacked against it, and how it is managing to not only weather the changes, but also distinguish itself by staying true to the values of the Triple Aim.
Reviewing the Healthcare Analytics Adoption Model: A Roadmap and Recipe for A...Health Catalyst
Dale Sanders provides an update on the Healthcare Analytics Adoption Model. Dale published the first version of this model in 2002, calling it the Analytics Capability Maturity Model. The three intentions at that time are the same as they are today: 1) Provide healthcare leaders with a clear roadmap for the progression of analytic maturity in their organization. 2) Provide vendors with a roadmap to meet the analytic needs of clients. 3) Create a common framework to benchmark the progressive adoption of analytics at the industry level.
In 2012, Dale co-published a new version of the Model with Dr. Denis Protti, rebranding it the Healthcare Analytics Adoption Model and purposely borrowing from the widespread adoption of the EMR Adoption Model (EMRAM) published and supported by HIMSS. In 2015, Dale transferred the model under a creative commons copyright to HIMSS to create a vendor-independent industry standard that is now widely applied to support the original three intentions. He continues to collaborate with HIMSS to progress the Model.
During this webinar, Dale:
-Reviews the current state of the Health Catalyst Model, including recent changes that advocate a ninth level—direct-to-patient analytics and AI.
-Shares his observations of maturity in the market.
-Provides an update on the current state of the HIMSS Adoption Model for Analytic Maturity.
Cloud computing promises to fundamentally transform the global life sciences industry. But most life sciences organizations have only just started to understand the power of cloud to not only drive efficiency, but also to redefine collaboration, partnering, and business models.
Life sciences organizations are hungry for the capabilities that cloud can deliver, to meet new competitive pressures and ever-expanding consumer expectations.
This new IBM Institute for Business Value (IBV) Cloud point-of-view (POV) for the life sciences industry explores the opportunities and implications of cloud computing for global life sciences companies. It provides a roadmap to formulate and execute cloud strategies.
Hospital Value-Based Purchasing: Leveraging Analytics for HVBP Prospective Pa...Perficient, Inc.
This document provides an overview of the Hospital Value-Based Purchasing (HVBP) program, including the timeline, measures, and calculation methodology. It discusses how HVBP rewards hospitals with incentive payments based on performance on clinical process, patient experience, outcome, and efficiency measures. The presentation agenda outlines covering HVBP overview, calculation, a demonstration of an analytics application, and Q&A. Eligible hospitals are acute care hospitals paid under the Inpatient Prospective Payment System, excluding those with deficiencies posing immediate jeopardy to patient health and safety.
The evolution of life science ecosystems: Five effective innovation approache...IBM in Healthcare
The life sciences industry, like many others, faces broad disruption and challenges on fronts ranging from technology to regulation to product resourcing. Traditionally, innovation has been a key driver of success for life sciences organizations, and it will continue to play a critical role for an industry that seeks to sustain this momentum.This report, the third of the Innovating Life Sciences series, identifies five strategies that differentiate the more successful academic life sciences institutions from the rest.
The Doctor’s Orders for Engaging Physicians to Drive ImprovementsHealth Catalyst
Physicians drive the majority of all quality and cost decisions, yet reimbursement pressures, competing time pressures, misaligned incentives, and a lack of credible data often make engaging clinicians in improvement work one of the biggest challenges in healthcare.
David Wild, MD, MBA, and Jack Beal, JD, explore how to spread data to the edges of the organization and engage physicians in leading a continuum of improvement across an entire organization.
During this webinar, our presenters:
• Identify the levels of physician leadership in your organization you can engage to drive improvement.
• Pinpoint the types of data and information of most interest to physician leaders.
• Propose several ways data to use data to engage physicians in leading improvement work.
• Help you develop at least one mechanism you can use to better engage physicians in improvement work at your organization.
How to Use Data to Improve Patient Safety: Part 2Health Catalyst
Stan and Valere will discuss how using an automated trigger tool for all-cause harm reviews will provide timely, real-time patient safety data useful to drive down harm rates with earlier interventions. Additional benefits of this approach include having a more accurate and robust source of data for identifying harm trends to then be able to integrate the findings into existing quality improvement processes for further quality improvement efforts.
Attendees will learn how to:
Understand the importance of dedicating resources to impact downstream costs
Identify their key sources of Patient Safety data
Integrate Patient Safety data in to existing Quality Improvement Processes
Learn and improve from real-time safety analytics combined with a Culture of Safety
The Top Five Essentials for Quality Improvement in HealthcareHealth Catalyst
Quality improvement in healthcare is complicated, but we’re beginning to understand what successful quality improvement programs have in common:
Adaptive leadership, culture, and governance
Analytics
Evidence- and consensus-based best practices
Adoption
Financial alignment
Although understanding the top five essentials for quality improvement in healthcare is key, it’s equally important to understand the most useful definitions and key considerations. For example, how different service delivery models (telemedicine, ACO, etc.) impact quality improvement programs and how quality improvement starts with an organization’s underlying systems of care.
This executive report takes an in-depth look at quality improvement with the goal of providing health systems with not only the top five essentials but also a more comprehensive understanding of the topic so they’re in a better position to improve quality and, ultimately, transform healthcare.
Go deeper with athenahealth specialists to discover all that you need to know and some things you may not know about Meaningful Use Stage 2 and the newest government updates.
How to Eliminate the Burden of Provider Quality Measurement: Able HealthHealth Catalyst
Quality measurement is complicated by incomplete data, calculations, visualizations, and workflows. As a result, quality measurement is a significant burden for medical groups. In fact, research that Health Affairs published in 2016 quantified the burden as 785 hours per provider per year.
That's why Health Catalyst is excited to introduce Able Health, the only quality measures solution that’s truly complete.
In this webinar, you’ll learn how Able Health combines all data, measures, visualizations, and workflows (monitor, improve, and submit) into one complete solution. Eliminating the complexity, and therefore the burden, of provider quality measurement means you spend more time improving performance and less time managing data.
You’ll also learn how each of the three core components of the Able Health solution makes more efficient quality measurement possible:
-Measures engine—calculates performance for all provider quality measures for all payer programs using every available data element.
-Performance dashboard—visualizes all performance metrics for daily tracking, prioritization, and internal reporting for all stakeholders, especially physicians.
-Submission engine—submits compliant data to payers.
SXSW Panel Picker slide show for Agile Development and the FDA
Developing software for the healthcare sector is difficult enough, but doing so under the scrutiny of the FDA can seem impossible. However, if you want to have an impact at the point of patient care the FDA is going to be a factor in your development. We will look at ways to marry the seemingly contradictory philosophies of Agile development, with its high efficiency, low documentation process, with the FDA regulated requirements of complete audibility and seemingly endless piles of paperwork. Through a real-life case study we will looks at modern software development practices through the lens of the FDA.
Apervita received Frost & Sullivan's 2015 New Product Innovation Award for its secure, self-service analytics platform that allows healthcare organizations to easily publish, access, and commercialize clinical decision support rules, quality measures, and other analytics. The platform addresses the growing need for affordable, customizable analytics solutions. Apervita received high scores in Frost & Sullivan's evaluation for its strong match to customer needs, ease of use, and ability to empower sharing of best practices.
Business Intelligence And Healthcare White Paper (English)smitchell1974
Major applications of business intelligence software in the healthcare industry include:
1) Financial analysis to reduce costs and ensure quality care
2) Quality performance and safety analysis to improve clinical processes and outcomes
3) Marketing analysis to better target performance goals and identify ways to improve care
Strategic Options for Analytics in HealthcareDale Sanders
There are essentially four analytic strategies available in the healthcare IT market at present. This slide summarizes those options, the pros and cons, and vendors in the space.
This document discusses acceptance test driven development (ATDD). It describes how ATDD involves first writing acceptance tests based on requirements before writing unit tests or code. This ensures requirements are clearly understood and tests provide feedback during development. ATDD tools like Concordion and FitNesse are mentioned for automating acceptance tests in a readable format. Benefits of ATDD include improved requirements understanding, early detection of failures, and reduced defects through continuous feedback.
In many web or cloud applications, performance testing is critical part of application testing since it affects
business revenue, credibility, and customer satisfaction. Conventional software development models are known
to pushing the performance testing to the very end of project, with the expectations that, only minor tweaks
and tune up are required to meet the performance requirements from the business, however any major
performance bottlenecks found during this phase were major factors for delay in Go to Market. With more and
more companies are adapting the agile software development process which believes in performance testing
should never be an afterthought but it should tightly integrate from initial planning to production analysis of
software development lifecycle. This white paper explains how any company can integrate performance testing
into agile process, and key barriers for agile performance testing when team decides to adopt agile performance
testing.
Introduction to Investigation And Utilizing Lean Test Metrics In Agile Softwa...IJERA Editor
The growth of the software development industry approaches the new development methodologies to deliver the
error free software to its end-user fulfilling the business values to product. The growth of tools and technology
has brought the automation in the development and software testing process, it has also increased the demand of
the fast testing and delivery of the software to end customers. Traditional software development methodologies
to trending agile software development trend have brought new philosophy, dimensions, and processes having
invested new tools to make process easy. The Agile development (Scrum, XP, FDD, BDD, ATDD, ASD,
DSDM, Kanban, Crystal and Lean) process also faces the software testing model crises because of the fast
development of life cycles, fast delivery to end users without having appropriate test metrics which make the
software testing process slow as well as increase the risk. The analysis of the testing metrics in the software
testing process and setting the right lean test metrics help to improve the software quality effectively in agile
process.
IRJET- Research Study on Testing Mantle in SDLCIRJET Journal
This document discusses the role and importance of testing in the software development life cycle (SDLC). It describes the typical phases of the SDLC, including requirement gathering, design, coding, testing, deployment, and maintenance. Testing is involved throughout the SDLC to improve quality, reliability, and performance. The key roles of testing include finding bugs, improving product standards, demonstrating feasibility, and avoiding faults migrating between phases. Testing helps deliver high quality software that meets requirements and manages risks.
Enabling Continuous Quality in Mobile App DevelopmentMatthew Young
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EMBEDDING PERFORMANCE TESTING IN AGILE SOFTWARE MODEL
1. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
DOI: 10.5121/ijsea.2021.12601 1
EMBEDDING PERFORMANCE TESTING IN
AGILE SOFTWARE MODEL
Suresh Kannan Duraisamy, Bryce Bass and Sai Mukkavilli
Department of Computer Science,
Georgia Southwestern State University, Americus, GA
ABSTRACT
In the last couple of decades, the software development process has evolved drastically, starting from Big
Bang to Waterfall to Agile. The primary driver for the evolution of the software was the “Speed of
Delivery” of the Software Product which has significantly accelerated from months to less than weeks and
days. For IT (Information Technology) Organizations to be successful, they inevitably need a strong
technology presence to roll out new software and features as quickly as possible to their customer base.
The current user generation tends to use technology to maximum potential and is always striving to keep
up with the new trends. The main subject is for the organizations to be ready with their Speed of Delivery
strategy adapting to all technology modernization initiatives like CICD (Continuous Integration and
Continuous Deployment), Agile, DevOps, and Cloud so that there are negligible customer friction and no
risks to their Market shares,. The aim of this paper is to compare the performance testing in every stage of
the agile model to the traditional end testing. The results of the corresponding testing phases are presented
in this paper.
KEYWORDS
Agile, CICD, Waterfall, Performance Testing.
1. INTRODUCTION
1.1. Problem Statement
Many a times while using a software product or websites during peak holiday seasons and during
ongoing high demand promotions, customers come across digital disruptions while using the
software products which impacts the end user experience. This issues are caused due to slowness
or stability concerns of the applications. That are pointed towards the volume of transactions that
the application Logic is not designed to handle or some time the capacity of the computation that
was required was not planned ahead of time.
Performance Issues usually have a high stake of all the tangent of an organization starting from
the Customer trust on using the product again, Total cost of Operations and Financial impacts and
the Overall brand reputation of the organization.
Waterfall model for software development is a well-known and was very widely used for more
than a decade for a software development process. The waterfall model has software
development phases accomplished in a sequential linear way which comprises of Requirement
Phases, Design, Implementation, Verification and Maintenance. Requirements are documented
and moving to next phase is dependent on the signoff of the prior phase. It usually takes time for
2. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
2
the end users before they can start using the software or partial features. Often time there are risks
that the requirements are deviated and considerable amount of rework has to be accomplished to
meet the end user’s needs.
Agile software development model is an iterative deliverable approach where software Product
are broken down into smaller features and are delivered incrementally in sprints [1]. Each sprint
has all the Phases incorporated starting from Design to Deployment and generally are delivered
within 2 to 4 weeks. In Agile the application is released to end users in continuously and the
feedbacks are incorporated in the upcoming sprints with the additional development of the
Product, this entire cycle of Sprint is repeated until the desired software product development is
completed.
Agile is more popular these days as the customer are more confident on the outcome as they have
a continuous feel of the product and can share feedbacks continuously to the software
development teams. One of the challenges of Agile Software development model is about
Addressing of Quality control Processes including Performance testing in the short sprint of time.
This work will be covering the problems and potential solutions that can be adapted to deliver
Performance readiness and ensure high stability of software applications in agile software
development model.
The primary Performance readiness requirement in agile is to ensure there is no disruptions to the
new features that are to be delivered in the sprint and the existing product features that the
customers are already using in the production.
1.2. Related Work
Andre et.al [1] talks about developing a common agile software development model. Also, Jun
Lin et.al [2] talk about modelling user stories in agile software development model. Marian et.al
[3] uses the agile vs traditional comparison to show the importance of the former. [6] talks about
the performance testing for developers which relates to the performance testing in agile
mentioned in this paper.
1.3. Finding Performance Issues late in the release
The Branching and Merging Strategy plays a vital role in the agile model that drives the
development velocity and the overall Release Cadence. Figure 1 has the List of typical activities
carried out in an agile development branch. Performance testing usually is pushed as much as to
the end of the sprint leaving less time to execute good test and resolve any Performance issues
that are introduced newly into that sprint. This Practice creates software application vulnerable to
new issues and instability thus causing unhappy end users that impacts the overall revenue of the
organization.
1.4. Lack of Automation in Performance Testing
Performance Testing [8] is manual driven and needs lot of human intervention in every step of
Performance test execution that includes Deployment of application code base in the Perf
environment, Smoke testing , test data preparations, Monitoring Instrumentation and running the
required type of Performance tests, and Result analysis and Tuning the Performance bottlenecks.
All these activities are time consuming and often struggle to fit within a short sprint release
cadence and causes Performance issues to be compromised in production.
3. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
3
1.5. Executing Agile Performance testing with dedicated Centralized Performance
Team
Performance Testing or any Testing practice in waterfall software model [3] used to be the final
gate before the release of the product which was easy for Centralized Performance Team to
execute. The way Performance testing was traditionally done with a dedicated Performance
testing also referred to as Center of Excellence doesn’t fits well in the rapid fast paced frequent
feedback based agile software development model.
2. POTENTIAL APPROACHES TO MITIGATE THE PROBLEMS
2.1. Shift Left Performance Testing
Let’s understand the problem deeper and for that we would need to deep dive in to The
Branching and Merging Strategy that plays a vital role in the agile strategy and drives the
development velocity and the overall Release Cadence. As seen in the below typical Agile
Delivery, throughout the development cycle there is a continuous integration of some feature
codes in to the application Master code base.
Positioning the Performance testing in Agile thus is very complicated considering
The Application and Scope is continuously changing and
There is a very short runway to complete the Performance testing on time
Figure 1. Agile Delivery Process
There are four possible gateways to enable the Performance testing in an agile sprint. The goal is
to push the Performance testing practice as much as left in the development sprint so that
Performance issues can be uncovered sooner to avoid pushing performance defects to backlog
and often Performance issues gets leaked in to production which has high business impacts – due
to stability concerns and end user experience dissatisfaction
4. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
4
Figure 2. Shift Left and Right Strategy
As displayed in Figure 2, the performance testing has to be moved as much as left –“Shift Left
strategy” that is having some level of testing at the feature branch level in the developer
environment with minimum expected baseline volume. This will give an insight of the high-level
performance of the code that is developed and will uncover any major open blockers that can
show up later in the sprint. To enable Shift Left Performance strategy there should be a good self
service capability where the developers can do effective performance testing at the feature branch
level easily before the newly developed code or feature branch is merged into the Master Code
base. Usually this kind of self-service testing tool are facilitated by the Performance teams or the
Tools teams. This testing can be focused to an API Level or service level or a specific
functionality corresponding to the newly developed feature to get the first insight of the
performance and Later the Performance team will exercise the rigor performance testing cycle in
the release branch with the full volume in the Production equivalent Performance test
environment.
2.2. Automation of Performance Testing
2.2.1. Integration in the CICD Pipeline
With the Speed, Agile delivery runs it’s not only hard to complete performance testing on time
but impossible for Performance engineers to keep up with the 100s of features that is pushed
continuously into pipeline. The Primary notion to handle the Performance testing in a CICD
Pipeline is that enable the Developers to do the Performance testing at the CI part of the pipeline
i.e. at the feature branch testing , And at the Continuous deployment stage which is more
applicable later in the cycle at the release branch level have as much as automation.
Automation in the Continuous deployment pipeline can be implemented using the plugins the
performance tool provides. It’s important for Performance Testing tool to integrate in the
Automation pipeline to have a successful automation in place. Performance test tools plugin has
to be installed and configured in the Pipeline Automation solution there by calling the test suite
after the deployment is completed in to the Performance environment’s Performance test job that
triggers the performance test suite is a point based SLA driven test suite that automatically sends
signal to the pipeline about Success or failure of the Performance and enables the continuity of
the pipeline.
5. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
5
Figure. 3. Deployment Phases
2.2.2. Test Environment and Data Dependencies
For an effective test cycle wither manual or automation it’s important to have a good test data
management including the volume of the test data that is very important for the accuracy of the
Performance testing results. Below are some of the important test data best practices that needs to
be covered to have a successful automation of performance testing.
Volume of the test data in the Performance environment should match the production
volume - A better mechanism is to have a regular refresh of production database and
have the process of export and import into perf environment automated.
There are huge number of scenarios that are data dependent and cannot be reused
iteratively with in scripts .These scenarios has to be identified and should have a good
plan in place to retrieve test data real time before the test is executed in pipeline and
should be able to feed in to scripts to have the test suite exercised without any disruptions
On the Performance Environment, it’s important to settle on a common methodology across
organization by standardizing Test tool, Test practice and solidifying on the environment needs.
The feature branch testing that is primarily driven by the individual developers must leverage the
local dev environment or can be facilitated by an on demand Virtual environment.
Later when the application is ready and deployable after the release branch is cut, the artefacts are
deployed into the Performance environment. The Performance environment should be 100%
equivalent to production capacity with as much as end to end component and logical flow
connected. Performance monitoring should be well instrumented in the Performance environment
to drill down any performance deviations noticed in the Performance environment.
6. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
6
2.2.3. Monitoring and Engineering Analysis
The Automation Pipeline should have all the monitoring capabilities, and this is possible only if
the monitoring and the Application Performance monitoring tools (APM) is integrated with the
Performance tools. The testing tool should be configured with the right defined Service level
indicators and Service level objectives and can communicate back to the pipeline with the status
of the performance testing.
Automated report that can be shipped as an email will expedite the Performance analysis process
that highlights any performance Service level agreement (SLA) deviations and this helps
performance engineers to easily pinpoint and start the deep dive analysis quickly and engage with
the partner team and start the triage at the earliest.
Figure 4. A template of the automated report.
2.3. Performance test Engagement model
Traditionally Performance testing was a centralized team that gets request for Performance
testing across organization and the Performance team prioritizes there task and report out the
execution results and any Performance issues that needs to be tuned back to the application
engineering teams. This approach may turn out a bottleneck for the entire agile delivery timelines
and Performance testing should be treated as a team sport with active collaboration between
Performance team and Application development team.
Usually to address this challenge it’s good to have Performance SME’s identified for each of the
scrum teams and they participate in the daily stand-ups and sprint planning to understand any
Performance impacts with the new stories that are planned for the upcoming release. This enables
the Performance team to plan of time and keep up the test suite when the release branch is cut.
Also, this identified performance engineers enables performance testing for each of the Scrum
team by facilitating the self-service performance test options to the developers.
In general, One to One Performance engineers mapped for each developer is not a cost efficient
model and transition of Performance team from an enterprise center of Excellence to center of
7. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
7
Enablement team can make the Performance testing in Agile scrum team more productive as
depicted in Figure 5. There will be a representative identified for each scrum team that will be
driving all the end to end perf needs including enabling shift left and preparation of Automating
Performance test execution in the pipeline.
Now that some of the general underlying problems are discussed about agile software
development model with incorporating Performance testing is discussed and potential solution
has been laid out. There are many pros and cons that comes up with the discussed solutions,
however the defined approach must be tailored based on the development ecosystem and the
system architecture considering the details that are discussed below.
3. MERITS AND RISKS OF THE PROPOSED APPROACH
3.1. Time to Market
Having a shift left Performance testing enables Performance issues to be uncovered at the
featured branch level and resolve earlier in the life cycle, this save lot of time and operational
expenses to the overall software development program. With the automated Performance testing
in the Continuous deployment pipeline enables the Performance findings to be shared quickly and
wrap up the Performance readiness signoff process sooner in the release sprints. Overall, these
two practices enable the speed of delivery in the agile software development model thus
increasing the time to market that the business can take benefit. Another important aspect where
the software can be delivered to the end users more.
3.2. End to End and Integration Gaps
Often Shift Left Performance testing are executed in a low scale environment with low
transaction volume and virtualized integration end points of transactions though this uncovers
high level performance issues and gives opportunity to developers to resolve performance issues
earlier before the code is committed to master. The Performance delays and issues that are tied to
the integration points or asynchronous services are not in scope of this testing. One of the
practices to mitigate to some extend is have a realistic delays with the virtualized endpoints and
to know about the latency patterns it needs some level of Production monitoring trend analysis
about the latency of these end points.
As discussed on the different merits and drawbacks about bringing in continuous performance
testing in the DevOPS practices, we also wanted to try out if this concept can be proven in a lab
setup where on demand environments can be spun and Performance testing process be automated
in the pipeline.
4. RESULTS
Following were the objectives that was defined for the proof of concept.
Terraform scripts that will be used to spin up a instance will be retrieved from GIT HUB
Jenkins Terraform job will spin a mock App instance on Amazon cloud (AWS) on
demand.
Same pipeline job will initiate another freestyle Jenkins job, which will run a test (test
suite) and hit the sample app instance created by the first JOB.
As soon as the test job is complete, there will be a performance test report that will be
shipped out as an email
8. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
8
Created instance will be destroyed by the Jenkins job after test suite is complete.
Figure 5. Tests being performed
Figure 6. Performance testing using CAVISSON
Below is the summary of the data that was captured about the time duration usually they spent to
complete a full end to end performance testing cycle. This data clearly depicts that it easily takes
about 2 to 4 days to get the performance findings out and these are matured technology
organizations with experts driving the performance testing with decades of experience.
9. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
9
Domain
Application-Techstacks
PolicyApps
JavaOracleTibco
EISTomcat
ClaimsApps
Java/Oracle/GW/Some
AWSAppcomponentsand
.netapps
Supplychain-Order
Management
JavaWeblogic
Mainframe DB2
F5
StoreOperations
Datapower
JsonRestservices
OracleJava
InternalContact
CenterApp
.NetThickClient
Apps,SQLServer
ConsumerBanking-
React/Openshift/AWS
APIGateway
JavaOCP,Oracle,
MDM-IBM,
Release Deployment
*CoordinationandTicketcreation 4to5Hrs. 2to4Hrs. 1day 1day 2to4Hrs. 2to3Hrs.
SmokeTestingandissueresolution
*Environment,functionalandDataissues 2days 1Hr. 4Hrs. 3Hrs. 3Hrs. 1day
PerformanceTesting
*including Datprep 2.5Hrs. 3Hrs. 2Hrs. 8Hrs. 2Hrs. 2Hrs.
ResultPreparation 2Hrs. 2Hrs. 3Hrs. 3Hrs. 1Hr. 2Hrs.
Analysis 4Hrs. 2Hrs. 1day 3Hrs. 1Hr. 2Hrs.
Numberofissues 6issues 4issues 1to2issue 2to4issue 1Issue 2to4issues
%ofnewissuesarerelatedtonewdevelopment ~50% ~25% ~50% ~50% ~50% ~50%
Featurebranchtesting No No No No No No
TotalTimefor1PerformanceTestingCycle ~4Days ~2Days ~2Days ~3days `2Days `3Days
Insurance Retail Banking
Figure 7. Performance testing cycle results
With the proposed Automation solution proposed, this testing result can be shipped out to the
stakeholders within an hour of deployment into performance environment. This includes spin up
of an on-demand environment using Terraform scripts in the AWS EC2 Instance followed by
running the Performance test suite and then shipping out the test report and finally tearing down
the OnDemand server
ALEXA APP JSON SCRIPT:
{
"manifest": {
"publishingInformation": {
"locales": {
"en-US": {
"summary": "Quiz for GSW",
"examplePhrases": [
"Alexa, open georgia southwestern quiz"
],
"keywords": [],
"name": "GSW Quiz",
"description": "This is a simple Quiz that tests your knowledge on Georgia
Southwestern University and Americus. To start, open the skill by saying,
u0027Alexa, open georgia southwestern quizu0027, and then after the welcome
message you will be prompted to say u0027start quizu0027. Enjoy.",
"smallIconUri": "file://assets/images/en-US_smallIconUri.png",
"largeIconUri": "file://assets/images/en-US_largeIconUri.png"
}
},
"automaticDistribution": {
"isActive": false
},
"isAvailableWorldwide": true,
"testingInstructions": "Say u0027open georgia southwestern quizu0027 to start the
skill and then u0027start quizu0027 to start the quiz. Itu0027s just a
simple quiz.",
"category": "KNOWLEDGE_AND_TRIVIA",
"distributionMode": "PUBLIC",
"distributionCountries": []
},
"apis": {
"custom": {
10. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
10
"endpoint": {
"uri": "arn:aws:lambda:us-east-1:289211917748:function:0903e819-07d9-49bd-9ea3-
0b44e6f8dd5c:Release_1"
},
"interfaces": [],
"regions": {
"EU": {
"endpoint": {
"uri": "arn:aws:lambda:eu-west-1:289211917748:function:0903e819-07d9-49bd-
9ea3-0b44e6f8dd5c:Release_1"
}
},
"NA": {
"endpoint": {
"uri": "arn:aws:lambda:us-east-1:289211917748:function:0903e819-07d9-49bd-
9ea3-0b44e6f8dd5c:Release_1"
}
},
"FE": {
"endpoint": {
"uri": "arn:aws:lambda:us-west-2:289211917748:function:0903e819-07d9-49bd-
9ea3-0b44e6f8dd5c:Release_1"
}
}
}
}
},
"manifestVersion": "1.0",
"privacyAndCompliance": {
"allowsPurchases": false,
"locales": {
"en-US": {}
},
"containsAds": false,
"isExportCompliant": true,
"isChildDirected": false,
"usesPersonalInfo": false
}
}
}
5. CONCLUSION AND FUTURE WORK
With this study the oncoming demand of Speed of delivery in software development lifecycle is
important that performance testing practice must be automated in the pipeline with automated
reports that can provide realtime feedback. I think the future of Automating performance testing
will be around Shift Left Automation. There are multiple ways the industry itself is looking at
this topic however the more automation in the left to enable the performance testing process as
early as in the software development lifecycle will be true success of this study. Also, some
limitations include lacking a proper testbed and software development process. Testing this
feature on a small environment versus a true software development may cause some results to
vary. Testing also takes up a significant amount of time in each and every phase which might
lead to the delay in production for larger software. This study can be deployed in testing small
projects and if successful, can be extrapolated on much bigger ones. This can be even used in app
development from a small scale app to much bigger and most downloaded ones like the one built
on Alexa by author 2 mentioned above.
6. ACKNOWLEDGEMENTS
We would like to thank the professors from the CS department at GSW for their continuous
support on this project. We would also like to thank AWS for allowing us to rent their instance,
the results of which were shown in this paper.
11. International Journal of Software Engineering & Applications (IJSEA), Vol.12, No.6, November 2021
11
REFERENCES
[1] André Janus (2018), “Towards a common Agile Software Development Model”, ACM SIGSOFT,
July 2012 Volume 37 Number 4.
[2] Jun Lin, Han Yu, Zhiqi Shen, Chunyan Miao (2014), “Using goal net to model user stories in agile
software development”, ACIS International Conference on Software Engineering, Artificial
Intelligence, Networking, and Parallel/Distributed Computing (SNPD).
[3] Marian Stoica, Marinela Mircea, Bogdan Ghilic-micu (2013), “Software Development: Agile vs.
Traditional”, Informatica Economică vol. 17, no. 4/2013.
[4] Srdjana Dragicevic, Stipe Celar, MiliTuric (2017), “Bayesian network model for task effort
estimation in agile software development”, Journal of Systems and Software Volume 127, May 2017,
Pages 109-119
[5] https://f.hubspotusercontent30.net/hubfs/7652530/10-best-practices-app-performance-testing-
071918.pdf
[6] https://softcrylic.com/blogs/performance-testing-for-devops/
[7] https://www.synopsys.com/blogs/software-security/continuous-testing-cicd/
[8] https://ieeexplore.ieee.org/abstract/document/4293621
AUTHORS
Suresh Kannan Duraisamy: Suresh is a Master’s student in the department of
Computer Science at GSW. Along with pursuing Masters, he is a full time IT
professional. SK also has more than 15 years of IT professional experience and is
Specialized in transforming QA teams to adapt to the Technology modernization
initiatives including Agile transformation, Cloud, Microservices, and Enabling
Performance and Functional testing processes in DevOps -SRE culture by instrumenting
Automation frameworks in the CI-CD pipeline.
Bryce Bass: Bryce is an undergraduate student in the department of Computer Science
and IT Analyst/Programmer at GSW. Along with his studies, he works at the University
IT office. He also developed the “GSW Trivia” Alexa app.
Sai Mukkavilli: Sai Mukkavilli is an Asst. Professor in the CS department at GSW. His
area of interest and research are Cloud computing and security. Bryce and Suresh are Dr.
Mukkavilli’s former students.