Evaluating web software reliability
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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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Evaluating web software reliability

CSI518 – Group 1

Evaluating Web Software Reliability

By Zumrut Akcam, Kim Gero, Allen Chestoski,

Javian Li & Rohan Warkad


Agile development

Agile Development


The agile development process

  • What is Agile?

    • Focus on business value

    • Collaboration within team; meetings

    • Communication is key

    • Sprints, prototypes, feedback

    • Changing requirements

The Agile Development Process


The agile development process1

  • 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


Overview

Overview


Definition of reliability

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


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, inaccessible files, JavaScript errors, etc.

    • User errors: improper usage, mistyped URLs.[2]

Failure Sources


Project goal

  • Attempt to extend previous work on testing the reliability of websites.

  • Gain experience doing a research project

Project Goal


Sprint 1

Sprint 1


Sprint 1 goals

  • Read relevant research papers

  • Identify factors that may effect reliability analysis

  • Gather status logs

Sprint 1 Goals


Factors that may effect reliability analysis

  • Web Workload Characteristics

    • Byte Count

    • User Count

    • Session Count

    • Error Count

Factors That May EffectReliability Analysis


System to analyze reliability on

  • 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


Sprint 2

Sprint 2


Sprint 2 goals

  • 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


Sprint 2 progress

  • DNN Logs (10 Websites)

  • PHP Logs

Sprint 2 Progress


What is dotnetnuke dnn

  • .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)


Our dnn logs

  • 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


Major problem

  • The DNN Logs do not contain

    • Session count

    • Byte count

Major Problem


Alternative

  • Generate our own DNN logs

Alternative


Sprint 3

Sprint 3


Server side

  • Technologies Used

    • Windows XP Professional

    • Microsoft Internet Information Services (IIS)

    • Microsoft SQL Server 2008

    • DotNetNuke (DNN)

Server Side


Generating logs

  • Clients

    • Web-Crawlers

    • DotNetNuke Client API

  • Inducing Errors

  • Logs contain:

    • Client IP’s

    • Byte Counts (Uploaded & Downloaded)

    • Time-Taken

    • Status Code

  • Generating Logs


    Description of success status codes

    Description of Success Status Codes


    Description of error codes

    Description of Error Codes


    Results

    Results


    Workload measurement facts

    • 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


    Status code bytes graphic

    Status Code-Bytes Graphic


    Error and success ratios

    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.


    500 internal server error profile

    500-Internal Server Error Profile


    Number of status codes

    Number of Status Codes


    Average time taken by different status codes

    Average Time Taken By Different Status Codes


    Identify a metric to analyze reliability

    • 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


    Conclusions

    • 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


    Conclusions1

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

    Conclusions


    Resources

    • [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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