This document discusses software reliability models and the Rayleigh model in particular. It explains that reliability models can be static or dynamic, and the Rayleigh model is a dynamic model based on a Weibull distribution. The Rayleigh model uses parameters estimated from project data to project defect rates. Higher defect rates during development generally correlate with higher field defect rates. More defects found and removed earlier in the process yields better quality. Accuracy of models depends on valid input data and establishing predictive validity for different organizations.
It is one of the topics of Software Engineering. Formal Approaches to SQA. It contains the information related to formal approaches and necessity of the approach.
This ppt covers the following topics
Software quality
A framework for product metrics
A product metrics taxonomy
Metrics for the analysis model
Metrics for the design model
Metrics for maintenance
Iterative model.
Spiral model
RAD(Rapid application development)
model.
Iterative model.
Spiral model
RAD(Rapid application development)
model.
A Water Fall Model is easy to flow.
It can be implemented for any size of project.
Every stage has to be done separately at the right time so you cannot jump stages.
Documentation is produced at every stage of a waterfall model allowing people to understand what has been done.
Testing is done at every stage.
This model was not the first model to discuss iterative development.
As originally envisioned, the iterations were typically 6 months to 2 years long.
Each phase starts with a design goal and ends with the client (who may be internal) reviewing the progress thus far.
Analysis and engineering efforts are applied at each phase of the project, with an eye toward the end goal of the project.
This model was not the first model to discuss iterative development.
As originally envisioned, the iterations were typically 6 months to 2 years long.
Each phase starts with a design goal and ends with the client (who may be internal) reviewing the progress thus far.
Analysis and engineering efforts are applied at each phase of the project, with an eye toward the end goal of the project.
This model was not the first model to discuss iterative development.
As originally envisioned, the iterations were typically 6 months to 2 years long.
Each phase starts with a design goal and ends with the client (who may be internal) reviewing the progress thus far.
Analysis and engineering efforts are applied at each phase of the project, with an eye toward the end goal of the project.
This approach carries less risk than a traditional Waterfall approach but is still far more risky and less efficient than a more Agile approaches.
In Iterative model, iterative process starts with a simple implementation of a small set of the software requirements and iteratively enhances the evolving versions until the complete system is implemented and ready to be deployed.
Iterative model.
Spiral model
RAD(Rapid application development)
model.
The first formal description of the waterfall model is often cited as a 1970 article by Winston W. Royce
Royce did not use the term "waterfall" in this article.
Royce presented this model as an example of a flawed, non-working model.
project scheduling: Project Scheduling in a project refers to roadmap of all activities to be done with specified order and within time slot allotted to each activity.
Project managers tend to define various tasks, and project milestones and they arrange them keeping various factors in mind.
project tracking:Periodic project status meetings with each team member reporting progress and problems
Evaluation of results of all work product reviews
Comparing actual milestone completion dates to scheduled dates
Comparing actual project task start-dates to scheduled start-dates
Informal meeting with practitioners to have them asses subjectively progress to date and future problems
Use earned value analysis to assess progress quantitatively
It is one of the topics of Software Engineering. Formal Approaches to SQA. It contains the information related to formal approaches and necessity of the approach.
This ppt covers the following topics
Software quality
A framework for product metrics
A product metrics taxonomy
Metrics for the analysis model
Metrics for the design model
Metrics for maintenance
Iterative model.
Spiral model
RAD(Rapid application development)
model.
Iterative model.
Spiral model
RAD(Rapid application development)
model.
A Water Fall Model is easy to flow.
It can be implemented for any size of project.
Every stage has to be done separately at the right time so you cannot jump stages.
Documentation is produced at every stage of a waterfall model allowing people to understand what has been done.
Testing is done at every stage.
This model was not the first model to discuss iterative development.
As originally envisioned, the iterations were typically 6 months to 2 years long.
Each phase starts with a design goal and ends with the client (who may be internal) reviewing the progress thus far.
Analysis and engineering efforts are applied at each phase of the project, with an eye toward the end goal of the project.
This model was not the first model to discuss iterative development.
As originally envisioned, the iterations were typically 6 months to 2 years long.
Each phase starts with a design goal and ends with the client (who may be internal) reviewing the progress thus far.
Analysis and engineering efforts are applied at each phase of the project, with an eye toward the end goal of the project.
This model was not the first model to discuss iterative development.
As originally envisioned, the iterations were typically 6 months to 2 years long.
Each phase starts with a design goal and ends with the client (who may be internal) reviewing the progress thus far.
Analysis and engineering efforts are applied at each phase of the project, with an eye toward the end goal of the project.
This approach carries less risk than a traditional Waterfall approach but is still far more risky and less efficient than a more Agile approaches.
In Iterative model, iterative process starts with a simple implementation of a small set of the software requirements and iteratively enhances the evolving versions until the complete system is implemented and ready to be deployed.
Iterative model.
Spiral model
RAD(Rapid application development)
model.
The first formal description of the waterfall model is often cited as a 1970 article by Winston W. Royce
Royce did not use the term "waterfall" in this article.
Royce presented this model as an example of a flawed, non-working model.
project scheduling: Project Scheduling in a project refers to roadmap of all activities to be done with specified order and within time slot allotted to each activity.
Project managers tend to define various tasks, and project milestones and they arrange them keeping various factors in mind.
project tracking:Periodic project status meetings with each team member reporting progress and problems
Evaluation of results of all work product reviews
Comparing actual milestone completion dates to scheduled dates
Comparing actual project task start-dates to scheduled start-dates
Informal meeting with practitioners to have them asses subjectively progress to date and future problems
Use earned value analysis to assess progress quantitatively
Continuous Integration (CI) environments cope with the repeated integration of source code changes and provide
rapid feedback about the status of a software project. However, as the integration cycles become shorter, the amount of data increases, and the effort to find information in CI environments becomes substantial. In modern CI environments, the selection of measurements (e.g., build status, quality metrics) listed in a dashboard does only change with the intervention of a stakeholder (e.g., a project manager). In this paper, we want to address the shortcoming of static views with so-called Software Quality Assessment (SQA) profiles. SQA-Profiles are defined as rulesets and enable a dynamic composition of CI dashboards based on stakeholder activities in tools of a CI environment (e.g., version control system). We present a set of SQA-Profiles for project management committee (PMC) members: Bandleader, Integrator, Gatekeeper, and Onlooker. For this, we mined the commit and issue management activities of PMC members from 20 Apache projects. We implemented a framework to evaluate the performance of our rule-based SQA-Profiles in comparison to a machine learning approach. The results showed that project-independent SQA-Profiles can be used to automatically extract the profiles of PMC members with a precision of 0.92 and a recall of 0.78.
ICEBERG: a different look at Software Project ManagementLuigi Buglione
Every project – whatever the application field – should be managed taking into account at least four dimensions: Time, Cost, Quality and Risk. To manage these dimensions, a key tool for a Project Manager is to increase project visibility, defined as the amount of information about the project associated with its probability of occurrence. This paper uses the “iceberg” metaphor to introduce the ICEBERG (Improvement after Control and Evaluation-BasEd Rules and Guidelines) approach that can help Project Managers through the use of standard (de jure and de facto) ICT methods and techniques. This approach focuses not only on the management, and measurement, of resources, process and product, but also of the project and the organization itself. A list of candidate measures related to these 5 entities is suggested for a comprehensive software measurement plan in order to reduce project risk.
Tenant-based resource allocation model for cost-effective scaling Software-as...Javier Mijail Espadas Pech
Computing resources are being transformed into a model consisting of services that are delivered in a similar way to traditional utilities such as water or electricity. One of the computing paradigms that have promised to deliver this utility computing vision is known as Cloud Computing. Cloud computing provides on-demand access to computational resources which together with pay-per-use business models, enables application providers seamlessly scaling their services. With cloud computing definition comes the term of elasticity which is the ability to create a variable number of virtual machine instances depending on the applications demands. Virtualization technology is widely adopted as an enabler of cloud computing because it provides benefits such as security, performance isolation, ease of management and flexibility of running within a user-customized environment. In the other hand, the cloud applications themselves have long been known to as Software as a Service (SaaS). SaaS is a software delivery paradigm where the software is hosted off-premises, developed by service providers and delivered via Internet and the payment mode follows a subscription model. For SaaS providers, having the power to scale up or down an application to only consume and pay for the resources that are required at certain point in time is an attractive capability and if done correctly it will be less expensive than running on traditional hosting. However, cost-effective scalability is not achieved just by deploying large-scale applications over pay-per-use cloud infrastructures, and idle processes and not used resources are wasted but charged to application providers. Over and under provisioning of cloud resources are still unsolved issues. Even if peak loads can be successfully predicted, without an effective elasticity model, costly resources are wasted during nonpeak times (underutilization) or revenues from potential customers are lost after experiencing poor service (saturation). In this sense, SaaS applications give an opportunity to improve this scenario due their multi-tenancy nature, which is the ability to offer one single application instance for several clients/providers (tenants). Each tenant can interact with the application as if it were an unique user and cannot access or view the data of another tenant. Consequently, with the use of cloud computing approaches such as on-demand virtual machine creation, it is possible to efficiently create a mechanism for SaaS applications in order to allocate, consume and charge only the required cloud computing resources by each tenant. This doctoral dissertation establishes formal measurements for under and over provisioning of virtualized resources in cloud infrastructures, specifically for SaaS platforms deployments and it proposes a resource allocation model to deploy SaaS applications over cloud computing platforms by taking into account their multi-tenancy thus creating a cost-effective scalable environment.
Six Sigma is
the powerpoint presentaion that i make during my 3rd yr. The format of
this presentation is truly professional. You can adopt this format for
your future presentations. You too can modify these. Alright.
So just keep going.
Live in flow
~rise and shine~
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Cotact: rvaidya67@hotmail.com
Linked-In: Vaidyanathan Ramalingam
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Cotact: rvaidya67@hotmail.com
Linked-In: Vaidyanathan Ramalingam
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Cotact: rvaidya67@hotmail.com
Linked-In: Vaidyanathan Ramalingam
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Agile Testing Leadership Lessons for the Test & QA Professionals
Silicon India Software Testing Conference - SOFTEC - 2 July 2011
Bangalore
Presentation from Speaker: Vaidyanathan Ramalingam,
Director Engineering (Test), Huawei Technologies R&D, Bangalore
Coverage:
1) Waterfall Testing Vs Agile Testing
2) Testing Checklist - 5W & 2H
3) Trade Off Economics in Testing
4) Software Testing Eco System
5) RCA (Root Cause Analysis)
Cotact: rvaidya67@hotmail.com
Linked-In: Vaidyanathan Ramalingam
SDLC
PDLC
Software Development Life Cycle
Program Development Life Cycle
Iterative model
Advantages of Iterative model
Disadvantages of Iterative model
When to use iterative model
Spiral Model
Advantages of Spiral model
Disadvantages of Spiral model
When to use Spiral model
Role of Management in Software Development
Customer oriented planning of case-tools using quality function deployment (qfd)Roy Antony Arnold G
Customer-Oriented Planning of CASE-Tools
Using Quality Function Deployment (QFD)*
Georg Herzwurm, Werner Mellis, Dirk Stelzer
Chair of Business Computing, University of Cologne, Prof. Dr. Mellis
Albertus-Magnus-Platz, 50923 Cologne, Germany
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Rayleigh model
1. 3/2/2011
Software Quality Management
Unit – 3
Roy Antony Arnold G
Asst. Prof./CSE
GRAA
• Software reliability models are
when it is available to the
customers.
• The criterion variable under study is the number of
defects in specified time intervals (weeks, months,
etc.), or the time between failures.
• Such an estimate is important for two reasons:
p
– (1) It is an objective statement of the quality of the
product
– (2) It is a resource planning tool for the software
maintenance phase.
GRAA
1
2. 3/2/2011
• Reliability models can be broadly classified into two categories:
and (Conte et al., 1986).
• A static model uses other attributes of the project or program
modules to estimate the number of defects in the software.
General Form :
The number of defects (y) is dependant on the attributes (x) of the
product and the process by which it is produced, plus some
error (e) due to unknowns which inherently exist.
• A dynamic model, usually based on statistical distributions, uses
the current development defect patterns to estimate end‐ end
product reliability.
• Dynamic Models are classified in two categories
– those that model the entire development process (Rayleigh Model)
– those that model the back‐end testing phase (Exponential Model
and Reliability Growth Models)
GRAA
• The Rayleigh model is a parametric model
in the sense that it is based on a specific
statistical distribution. It is a dynamic
reliability model.
• When the parameters of the statistical
distribution are estimated based on the
data from a software project, projections
about the defect rate of the project can be
made based on the model.
GRAA
2
3. 3/2/2011
• The Rayleigh model is a member of the family of the
.
• One of its marked characteristics is that the tail of its
probability density function approaches zero
asymptotically, but never reaches it.
• Weibull distributions are used for predicting reliability and
probability distribution
• Two standard functions for graphing Weibull
Two standard functions for graphing Weibull
GRAA
• Rayleigh is a special case of the Weibull
where the shape parameter (m) equals 2:
• The formulas represent a standard distribution.
• The total area under the curve is 1.
GRAA
3
4. 3/2/2011
The defect rate observed during the development process is
positively correlated with the defect rate in the field. (Fig.)
Assuming the d f removal effectiveness remains unchanged, then
A i h defect l ff i i h d h
a higher curve (more defects) during development means a higher
defect injection rate and hence a higher field defect rate.
GRAA
Given the same error injection rate, if more defects are
discovered and removed earlier then fewer will remain in
later stages and the field quality will be better.
– In the fig. the areas under the curves are the same but the curves
peak at varying points. Curves that peak earlier have smaller areas
at the tail, the GA phase.
In short, “Do it right the first time.”
This means that if each step of the
development process is executed properly
with minimum errors, the end product's
quality will be good.
GRAA
4
5. 3/2/2011
• Most statistical software packages support
Weibull Distributions.
• Applications can be developed due to the
clearly defined algorithms for Weibull.
• COTS (Commercial Off The Shelf) products
can also be used:
GRAA
• Accuracy of model estimates.
• Input data must be accurate and reliable.
• To establish high Predictive Validity,
and empirical validity must be
established.
• The validity of software reliability models
. A certain model may work well for a
specific organization or development structure but
structure,
not for others.
• No universally good software reliability model
exists.
GRAA
5