1. Evaluating Web Software Reliability Based on Workload and Failure Data Extracted From Server Logs CSI518 – Group 1 By Zumrut Akcam, Kim Gero, Allen Chestoski, Javian Li and Rohan Warkad

2. Definition of Reliability The increasing usage of Web software systems and attraction of society to the Web makes reliability of Web systems more important. What is reliability for Web applications? The reliability for Web applications can be defined as the probability of failure-free Web operation completions.[1] Failure is “the event of a system deviating from its specified behavior like obtaining or delivering information”.[2]

3. Failure Sources Failures are caused from the following sources: Host, network or browser failures: computer systems, network or software failures, etc. Source content failures: missing, unaccessible files, Javascript errors, etc. User errors: improper usage, mistyped URLs. [1]

4. Project Goal This project concentrates on the minimizing of source content failures to strengthen the reliability of Web applications. With this project, we try to accomplish the following goals as a team: Attempt to extend work on testing the reliability of websites. Gain experience doing a research project

5. Workload Analysis To analyze the reliability of web systems, we're gooinh to use the access logs and error logs under the title of server logs. Failure information alone is not enough for assess the reliability of system so measuring the workload is also necessary. The measurements for workload are byte count, user count, session count and number of hits.

6. Workload Measures Hit Count: Each hit shows the specific request to a web server. Misleading because individual hits show high variability. Byte Count: Number of bytes transferred gives finer granularity than hit count. User Count: Treat each client IP address as one user. Disadvantages: coarse granularity. Session Count: Number of user sessions can be calculated by IP address and access times using time limits per user [3].

7. Sprint 1 Goals Read relevant research papers Identify factors that may effect reliability analysis Determine a system to analyze reliability on Identify a metric to analyze reliability Gather access and error logs

8. Relevant research papers 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 http://dx.doi.org/10.1007/s10664-008-9084-6 Jeff Tian, Sunita Rudraraju, 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 We hope to extend work on these papers

9. Factors that may effect reliability analysis Byte count User count Session count Error count

10. System to analyze reliability on Reliability analysis via error logs Variety of reliability requirements Commercial and non-commercial We will try to record the technologies the websites employ (Apache, DNN, ISS, PHP, Codefusion, etc..)

11. The Nelson Method R = (n-f)/n = 1 – (f/n) = 1 – r R: Reliability f: Total number of failures n: Number of workload units r: Failure rate Mean Time Between Failures (MTBF) MTBF = (1/f) Σ i t i MTBF = n/f ● Identify a metric to analyze reliability

12. Access and error logs Universities and companies refusing to provide us with access and error logs. Confidentiality reasons Outsourcing server management to external companies

13. Sprint 2 Goal Collect enough log files for calculation Automate processes to extra data (user, session, byte, and error counts) and convert them into excel format Log Parser

14. Sprint 2 Progress A web developer agree to send all logs he has (ASP.NET / DNN)

15. What is DotNetNuke (DNN) Founded 2006.NET version of Drupal an open source platform for building web sites and web applications based on Microsoft.NET technology. Leading open source ASP.NET web content management system and.NET development framework ~100 employees has been downloaded over 6 million times 5 th Version

16. Our DNN Logs 10 Websites Window Server (Same Server) SQL Server 2008 ~1000 unique visiters per day Logs contain User count Little Error count Doesn't contain Session count Byte count

17. Sprint 2 Problems Still looking for logs and may have to consider generating our information To create our own logs is under discussion

18. LogParser Microsoft tool designed for parsing text-based logs Flexible Support for common log file formats SQL style queries allow for targeted data extraction Can output to.csv Ease of bulk parsing Operates off Windows command prompt

19. Unique Client-IP’s for current logs

20. Sprint 3 and Beyond

21. Information Extraction Plot graphs and charts on parsed data Mine the data and derive relations Reliability models Why Nelson Model? Calculate Operational Reliability R = (n-f)/n = 1-r MTBF = (1/f)Σt i

22. Conclusion Derive key factors affecting reliability Provide Inputs Validating previous research Pointers for topics to explore for future research Detailed documentation and publishing

23. References [1] J.Tian, S.Rudraraju, Z.Li, “Evaluating Web Software Reliability Based on Workload and Failure Data Extracted from Server Logs”,2004. [2] T.Huynh, J.Miller, “Another viewpoint on 'Evaluating Web Software Reliability Based on Workload and Failure Data Extracted from Server Logs'”,2008. [3] G. Albeanu, A. Averian, I. Duda, “Web Software Reliability Engineering”,2009.