Software Analytics: Opportunities and Challenges Olga Baysal - - PowerPoint PPT Presentation

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Software Analytics: Opportunities and Challenges Olga Baysal - - PowerPoint PPT Presentation

Sep 16, 2022 286 likes •728 views

Software Analytics: Opportunities and Challenges Olga Baysal Latifa Guerrouj School of Computer Science Dpartement de GL et des TI Carleton University cole de Technologie Suprieure olga.baysal@carleton.ca

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Software Analytics: Opportunities and Challenges

Olga Baysal

  • School of Computer Science

Carleton University

  • lga.baysal@carleton.ca
  • lgabaysal.com

@olgabaysal Latifa Guerrouj

  • Département de GL et des TI

École de Technologie Supérieure latifa.guerrouj@etsmtl.ca latifaguerrouj.ca @Latifa_Guerrouj

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Development Data

Development (Big) Data

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Mining Software Repositories

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Docs

Other artifacts

Issues Mailing lists

Source code information

Code Tests Build/ Config Version Control

Runtime data

Crash repos Field logs

Usage data Detection and analysis of hidden patterns and trends

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Problem

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Development artifacts Stakeholders

Data Is NOT Actionable

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Decisions Drive Development!

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QA Developers Managers Release engineers

Program correctness Product deadlines Risks, cost, operation, planning Program defects When do we release? Are we ready to release? How do I fix this bug? How effective is our test suite?

Decisions are often based on intuition or experience

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Solution – Analytics

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Analytics In Industry

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Software Analytics

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Benefits of Software Analytics

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Supporting Development Decisions

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Data-driven decision making, fact-based views of projects

QA Developers Managers Release engineers

Product deadlines Risks, cost, operation, planning Program defects When do we release? Are we ready to release? How do I fix this bug? How effective is our test suite? Program correctness

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Software Artifacts

  • Source Code
  • Execution Trace
  • Development History
  • Bug Reports
  • Code Reviews
  • Developer Activities
  • Software Forums
  • Software Microblogs

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Artifact: Source Code

  • Various languages
  • Various kinds of systems
  • Various scale: small, medium, large
  • Various complexities
  • Various programming styles

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Artifact: Source Code

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Artifact: Source Code

  • Where to find code?

– GitHub: https://github.com/ – Google code: http://code.google.com/ – Many other places online

  • How to analyze source code?

– Program analysis tools

  • WALA: http://wala.sourceforge.net
  • JPF: http://javapathfinder.sourceforge.net/
  • Soot: http://sable.github.io/soot/
  • Clang: http://clang-analyzer.llvm.org/
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Artifact: Execution Traces

  • Information collected when a program is run
  • What kind of information is collected?

– Sequences of methods that are executed – State of various variables at various times – State of various invariants at various times – Which components are loaded at various times

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Artifact: Execution Traces

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Caller | Callee | Method Signature

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Artifact: Execution Traces

  • How to collect?

– Insert instrumentation code – Execute program – Instrumentation code writes a log file

  • What tools are available to collect traces?

– Daikon Chicory: http://groups.csail.mit.edu/pag/

daikon/dist/doc/daikon.html

– PIN: 


http://software.intel.com/en-us/articles/pin-a- dynamic-binary-instrumentation-tool

– Valgrind: http://valgrind.org/


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Artifact: Development History

  • What code is

– Added – Deleted – Edited

  • When
  • By Whom
  • For What Reason

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Artifact: Development History

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Artifact: Development History

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Artifact: Development History

  • Various tools

– CVS – Version per file – SVN – Version per snapshot – Git – Distributed

  • Slightly different ways to manage content

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Artifact: Bug Reports

  • People report errors and issues that they

encounter in the field

  • These errors include:

– Description of the bugs – Steps to reproduce the bugs – Severity level – Parts of the system affected by the bug – Failure traces

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Artifact: Bug Reports

  • Various kinds of bug repositories

– BugZilla: http://www.bugzilla.org/

Example site: 
 https://bugzilla.mozilla.org/

– JIRA: http://www.atlassian.com/software/jira/

Example site: https://issues.apache.org/jira/browse

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Artifact: Bug Reports

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Title

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Artifact: Bug Reports

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Detailed Description

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Artifact: Code Reviews

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Artifact: Code Reviews

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Artifact: Code Reviews

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Artifact: Developer Activities

  • Developers form a social network

– Developers work on various projects – Projects have various types, programming

languages and developers

– Developers follow updates from various

  • ther developers and projects
  • A heterogeneous social network is formed

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Artifact: Developer Activities

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  • GitHub’s CEO, Tom Preston-Werner:

“We like the ideas of social networking. We think that developers work more effectively when they work together. So let’s take the ideas of a social network and add on top of that code hosting, and let’s create a site that makes it easy to share and collaborate on code”.

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Artifact: Developer Activities

There are more than 12M people collaborating right now on GitHub on over 31M projects using a powerful collaborative development workflow.

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Artifact: Software Forums

  • Developers ask and answer questions
  • About various topics
  • In various threads, some of which are very long
  • Stored in various sites

–StackOverflow: http://stackoverflow.com/ –SoftwareTripsAndTricks: http://

www.softwaretipsandtricks.com/forum/

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Artifact: Software Forums

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Artifact: Software Forums

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Artifact: Software Microblogs

  • Developers microblog too
  • Developers microblog about various activities:

– Advertisements – Code and tools – News – Q&A – Events – Opinions – Tips – Etc.

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Artifact: Software Microblogs

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Opportunities

  • Help developers/managers to understand their

projects, cope with their evolution, and support them during their decision-making.

  • Extract relevant & insightful information, analyze

it, and transform to decisions for the future.

  • Find trends, anticipate issues, and bring

awareness on weaknesses or conditions for making future decisions.

  • Make proactive decisions using proactive

analytics: predictive modelling, data mining, machine learning, statistical analysis, etc.

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Opportunities

  • Leading tech. companies need insights to

create actionable tools, increase quality, efficiency, services and risk management.

  • Organizations apply analytics to create
  • pportunities for growth, innovation and

competitive advantage.

  • Data analytics identify patterns, trends and
  • pportunities for improvement, enabling to

spot which initiatives work, which fail, and to adjust accordingly.

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Challenges

  • SE data without explicit format.
  • SE data is plentiful.
  • Acting on results from data analysis is not easy.
  • Analytic tools? May be but should meet the need and

be easy to use.

  • Adoption of software analytics into software

development processes.

  • Development and integration of analytics tools in

practical settings.

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