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Evaluating Web Software Reliability

CSI518 – Group 1. Evaluating Web Software Reliability. By Zumrut Akcam, Kim Gero, Allen Chestoski, Javian Li & Rohan Warkad . Agile Development. What is Agile ? Focus on business value Collaboration within team; meetings Communication is key Sprints, prototypes, feedback

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Evaluating Web Software Reliability

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  1. CSI518 – Group 1 Evaluating Web Software Reliability By Zumrut Akcam, Kim Gero, Allen Chestoski, Javian Li & Rohan Warkad

  2. Agile Development

  3. What is Agile? • Focus on business value • Collaboration within team; meetings • Communication is key • Sprints, prototypes, feedback • Changing requirements The Agile Development Process

  4. How does Agile work? • How did our class use Agile? • Three Sprints • “Stand up” meetings at beginning of each class • Retrospective at the end of each sprint The Agile Development Process

  5. Overview

  6. What is reliability for Web applications? • The reliability for Web applications can be defined as the probability of failure-free Web operation completions.[2] • Failure is “the event of a system deviating from its specified behavior like obtaining or delivering information”.[1] Definition of Reliability

  7. Failures are caused from the following sources: • Host, network or browser failures: computer systems, network or software failures, etc. • Source content failures: missing, inaccessible files, JavaScript errors, etc. • User errors: improper usage, mistyped URLs.[2] Failure Sources

  8. Attempt to extend previous work on testing the reliability of websites. • Gain experience doing a research project Project Goal

  9. Sprint 1

  10. Read relevant research papers • Identify factors that may effect reliability analysis • Gather status logs Sprint 1 Goals

  11. Web Workload Characteristics • Byte Count • User Count • Session Count • Error Count Factors That May EffectReliability Analysis

  12. Reliability analysis via status logs • Variety of reliability requirements • Commercial and non-commercial • We will try to record the technologies the websites employ (Apache, DNN, ISS, PHP, ColdFusion, etc..) System to Analyze Reliability On

  13. Sprint 2

  14. Collect log files for calculation • Automate processes to extra data (user, session, byte, and error counts) • Convert them into excel format • Log Parser Sprint 2 Goals

  15. DNN Logs (10 Websites) • PHP Logs Sprint 2 Progress

  16. .NET version of Drupal • An open source platform for building websites and web applications based on Microsoft .Net technology. • Leading open source ASP.NET web content management • Has been downloaded over 6 million times • ~100 employees • 5th Version • Founded 2006 What is DotNetNuke (DNN)

  17. Logs from 10 Websites • Window Server (Same Server) • SQL Server 2008 • ~1000 unique visitors per day • Logs contain • User count • Limited Error count Our DNN Logs

  18. The DNN Logs do not contain • Session count • Byte count Major Problem

  19. Generate our own DNN logs Alternative

  20. Sprint 3

  21. Technologies Used • Windows XP Professional • Microsoft Internet Information Services (IIS) • Microsoft SQL Server 2008 • DotNetNuke (DNN) Server Side

  22. Clients • Web-Crawlers • DotNetNuke Client API • Inducing Errors • Logs contain: • Client IP’s • Byte Counts (Uploaded & Downloaded) • Time-Taken • Status Code Generating Logs

  23. Description of Success Status Codes

  24. Description of Error Codes

  25. Results

  26. Server log data consisted of 23 consecutive days of data. • Page Not Found (Error 404) is the most common type of error in our logs, with 46% of total recorded errors. • Accessing forbidden data (Error 403) follows with 41%. • 72 unique IPs, 33 thousand hits total, and each hit associated with about 5kb. Workload Measurement Facts

  27. Status Code-Bytes Graphic

  28. S U C C E S S E R R O R Error and Success Ratios *HTTP Status Codes defined By W3C-World Wide Web Consortium.

  29. 500-Internal Server Error Profile

  30. Number of Status Codes

  31. Average Time Taken By Different Status Codes

  32. The Nelson Model • R: Reliability • f : Total number of failures • n: Number of workload units • r: Failure rate • Mean Time Between Failures (MTBF) • MTBF = n/f Identify a metric to analyze reliability

  33. By Nelson Model, the site software reliability is R = 0.966, or that 96.6% of access to website is successful. • This model also shows that MTBF=29.6 hits or the site averages one error for every 29.6 hits. • From the number of errors chart, we can see that Server errors are very few among the other errors which shows what the reliability of the DNN server is. Conclusions

  34. [1] J.Tian, S.Rudraraju, Z.Li, “Evaluating Web Software Reliability Based on Workload and Failure Data Extracted from Server Logs”,2004. Conclusions

  35. [1] Jeff Tian, SunitaRudraraju, and Zhao Li. 2004. Evaluating Web Software Reliability Based on Workload and Failure Data Extracted from Server Logs. IEEE Trans. Softw. Eng. 30, 11 (November 2004), 754-769. DOI=10.1109/TSE.2004.87 http://dx.doi.org/10.1109/TSE.2004.87 • [2] Toan Huynh and James Miller. 2009. Another viewpoint on "evaluating web software reliability based on workload and failure data extracted from server logs". Empirical Softw. Engg. 14, 4 (August 2009), 371-396. DOI=10.1007/s10664-008-9084-6 http://dx.doi.org/10.1007/s10664-008-9084-6 • [3] G. Albeanu, A. Averian, I. Duda, “Web Software Reliability Engineering”,2009. Resources

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