Msr2016 tarek
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This document discusses using Application Performance Management (APM) tools to detect performance regressions in web applications. It presents a case study where performance regressions were injected into test systems and then evaluated whether commercial and open source APM tools could detect the issues. The study found that APM tools can successfully detect some performance regressions, but they have limitations like producing large reports that require manual exploration and lacking actionable suggestions for fixes. The document concludes that APM tools show promise as a way to deploy performance regression detection research into practice.
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- Automated performance testing is presented as more effective than manual testing due to issues with resources, coordination, and repeatability when using human testers.
Performance testing : An Overview
The document summarizes the results of performance testing on a system. It provides throughput and scalability numbers from tests, graphs of metrics, and recommendations for developers to improve performance based on issues identified. The performance testing process and approach are also outlined. The resultant deliverable is a performance and scalability document containing the test results but not intended as a formal system sizing guide.
Performance Testing
Performance testing is done to determine a system's responsiveness under different loads. It aims to optimize user experience. Types of performance testing include load, stress, soak/endurance, volume, scalability, and spike testing. The goals are to assess production readiness, compare platforms, evaluate configurations, and check against criteria. Pre-requisites include a stable test environment similar to production. The testing process involves establishing baselines and benchmarks, running tests, and analyzing results to identify bottlenecks and decide on fixes. Common issues relate to servers, databases, networks, and applications. Optimization involves improvements, upgrades, and tuning. Challenges include setting up the test environment and analyzing large amounts of test data.
What are the Characteristics of High-rated Apps
This document presents a case study analyzing factors that contribute to high and low ratings of free Android apps. It examines 28 factors across dimensions like app size, code complexity, library dependence, UI complexity, user requirements, and marketing efforts. The study finds that high-rated apps differ significantly from low-rated apps in 17 factors. The top 3 most influential factors for predicting ratings are app size, number of promotional images, and target SDK version. While some prior studies identified other factors like library quality and UI complexity, this multi-factor analysis shows those are less important than size and marketing efforts. Future work could explore additional factors and conduct more fine-grained analysis by app category.
Workload Analysis
This document summarizes the key points from a workload analysis report. It outlines selecting tools for monitoring and testing workload performance, characterizing the workload, analyzing for pitfalls, and reporting results to stakeholders. Specific tools mentioned include New Relic, DataDog, and Grafana for monitoring response times, throughput, and resource usage. The analysis covered traffic patterns, user behavior characterization, and defining a test scenario that ramps load up and down over time. Common pitfalls discussed are assuming the 90th percentile matches production and not accounting for differences when extrapolating horizontal scaling.
Continuous Performance Testing and Monitoring in Agile Development
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3) Establish best practices like monitoring all applications, testing continuously, and performance testing early to detect issues before production and expedite remediation.
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The document discusses the importance of performance testing applications. It notes that 47% of consumers expect a page to load within 2 seconds and 40% will abandon a site that takes over 3 seconds to load. Performance testing is important when high loads are expected, large data volumes are involved, or applications feel sluggish. An example performance testing project is described that aims to support 4000 users with responses under 5 seconds and conduct stress testing. Key aspects of performance testing like load testing, tools, test planning, analysis and best practices are also covered.
From web interface to the database:Monitor all that matters
Learn how to track key operational metrics of your Node.js and PHP infrastructure in real-time and get insight into the nuances of autonomous databases.
Sfeldman performance bb_worldemea07
The document discusses performance engineering at Blackboard, including defining key concepts like performance, scalability, and the application performance index (Apdex). It outlines Blackboard's performance engineering process and methodology, including using tools like LoadRunner for testing and establishing performance archetype ratios to measure scalability. Planned performance engineering projects for 2007 are also mentioned, such as virtualization testing and monitoring initiatives.
MELJUN CORTES research tcu_student_metro_south_abstract_thesis_bscs_llames_ma...
1) The document describes the development of an online management system for inventory control of vegetable production. It aims to lessen wastage of vegetables and provide updates to inventory through notifications.
2) The system was tested and evaluated based on criteria like functionality, reliability and usability. Users and IT professionals rated it highly based on weighted mean scores.
3) Screenshots show interfaces for tasks like sign up, inventory control, task management and notifications. The system was concluded to be useful for businesses to better manage inventory and reduce vegetable wastage.
Performance Aware Development
As the products and organizations grow in terms of scale, developing the applications while keeping performance an integral part of the build process is important. The presentation covers about what is performance aware development, why it is the need of the hour and how we are doing it inside LinkedIn where we run hundreds of services having multiple deployments everyday while making sure the performance of the services is kept in check and probability of introducing performance regressions is kept to a minimum.
requirement analysis characteristics
The document discusses the importance of requirements analysis and outlines desired skills and characteristics of effective requirements analysts. It notes that 53% of industry investments are lost to cost overruns and failed projects due to issues with requirements like incompleteness and changes. Effective requirements practices like collaborating with customers, managing requirements changes, and using automated tools can help reduce rework costs that typically account for 45% of projects. The document provides recommendations for training requirements analysts and establishing requirements processes and management commitment to support successful requirements definition.
Sech1920 1200112979886874-3
The document provides an overview of reliability metrics, hazard analysis stages, critical systems development techniques, verification and validation processes, types of testing, software inspections, and static analysis. It discusses reliability metrics like availability, probability of failure on demand, and mean time to failure. It also outlines hazard identification, risk analysis, and fault tolerance techniques like fault recovery and fault-tolerant architectures.
Performance Testing from Scratch + JMeter intro
Why performance testing is important?
Introduction to performance testing. Load profiles, metrics
Performance testing tools
Implementation process
Performance testing engineer skills
JMeter intro
LINKS:
About performance testing:
http://docplayer.net/29696161-Performance-matters-key-consumer-insights.html
http://www.softwaretestingclass.com/what-is-performance-testing/
https://www.blazemeter.com/blog
http://revolutionit.com.au/performance-testing-digital-projects-dont-leave-it-to-the-developers/
https://www.youtube.com/embed/61Kkgnx-zF8?list=PLSjEh0z5QH9lKpMO4Nf3buFiP7tt0gTRN
Concurrent users calculation:
https://blog.xceptance.com/2013/07/26/concurrent-users-the-art-of-calculation/
https://techbeacon.com/how-many-virtual-users-do-i-need-load-testing
https://www.webperformance.com/library/tutorials/CalculateNumberOfLoadtestUsers/
Browser performance:
https://www.slideshare.net/nicjansma/measuring-real-user-performance-in-the-browser
Books for start:
“Web Load Testing For Dummies”, Scott Barber with Colin Mason
“JMeter Cookbook”, Bayo Erinle
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Msr2016 tarek
- 1. 1 Studying the Effectiveness of Application Performance Management (APM) Tools for Detecting Performance Regressions for Web Applications Tse-Hsun Chen Ahmed E. Hassan Weiyi ShangTarek M. Ahmed Cor-Paul Bezemer An Experience Report
- 2. 2
- 3. 3 $1.6 billion loss for a one-second slowdown
- 4. What is a performance regression? 4 Version 1 Version 2 Does version 2 have worse performance than the version 1?
- 5. Performance regression: changed source code + unchanged workload change in performance? 5 Version 1 Version 2 requests requests requests Performance metrics Performance metrics Unchanged workload Testing environment Testing environment Changed source code Change in performance?
- 6. MSR techniques are widely used to detect performance regressions 6
- 7. MSR techniques are widely used to detect performance regressions 7 Building statistical models Mining data associationsComparing distributions
- 8. An example of using MSR techniques to detect performance regressions 8 Version 1 Version 2 Build model Verify model Model Prediction error < threshold > threshold Shang et al. ICPE 2015
- 9. The extensive performance regression detection research is hardly adopted in practice 9 Too complex to implement by practitioners Not easily accessible
- 10. Application performance management (APM) tools are well adopted in practice 10
- 11. APM tools work with: unchanged source code + changing workload 11 requests requests requests Real users Field environment Slow transactions Changing workload Unchanged source code
- 12. 12 An example report from an APM tool Most time consuming transactions Time breakdown for the transaction
- 13. 13 Performance regressions detection research + unchanged workload changed source code Not well adopted unchanged source code + changing workload Widely adopted
- 14. Research Questions: Can APM tools detect performance regressions? Can APM tools be used as a vehicle to deploy performance regression detection research in practice? 14
- 15. Case study: subject systems and injected performance regressions CPU overhead Memory overhead 15 Inefficient use of database • Unreleased resources • Inefficient use of data streams • Unnecessary loop • Loop with a missing break condition • One-by-one processing • Excessive data
- 16. We leverage APM tools to detect injected performance regressions Slow transactions in new version Slow transactions in old version 16 Version 1 Version 2 requests requests requests Unchanged workload Testing environment Testing environment Changed source code
- 17. 0 1 2 3 4 5 6 APM tools can detect performance regressions 17 #ofdetected issuesdetected Commercial Open source Total 6 issues injected
- 18. APM tools have rooms of improvement 18 APM tools are a great vehicle to push performance regression detection research into practice. Large amounts of reports for manual exploration Basic analysis Lack of actionable suggestion
- 19. 19 $1.6 billion loss for a one-second slowdown
- 20. 20
- 21. 21 unchanged source code + changing workload Widely adopted Performance regressions detection research changed source code + unchanged workload Not well adopted
- 22. 22
- 23. Case study: subject systems and injected performance regressions CPU overhead Memory overhead 23 Inefficient use of database • Unreleased resources • Inefficient use of data streams • Unnecessary loop • Loop with a missing break condition • One-by-one processing • Excessive data
- 24. 24
- 25. 0 1 2 3 4 5 6 APM tools can detect performance regressions 25 #ofdetected issuesdetected Commercial Open source Total 6 issues injected
Editor's Notes
- Fix pic size one for ahmed Make the talk short
- Slide nuber
- Slide number
- They are test separately
- Black outline
- Reasons to pick them. There is a survey. Leader for the field.
- Cloud scale ecommerce with tpcW
- Make sure to related to petclinic Add cloudscale
- Slide number
- Rework email
- Rework email
- Cloud scale ecommerce with tpcW
- Rework email
- Make sure to related to petclinic Add cloudscale
- Rework email