sociotechnical mining information from software …...6) change prediction 2014 marco aurélio...
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Mining Sociotechnical
Information From Software
Repositories
UNIVERSITY OF SÃO PAULO, BRAZIL
UFUNovember/2014
Marco Aurélio Gerosa
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Repositories of repositories
2014 Marco Aurélio Gerosa ([email protected]) 2
https://github.com/about/presshttp://octoverse.github.com/
11.3 million repositories5.4 million usersIn 2013:• 3 million new users• 152 million pushes• 25 million comments• 14 million issues• 7 million pull requests
36K projectshttp://en.wikipedia.org/wiki/CodePlex
30K projectshttps://launchpad.net
324K projects3.4 million developers
http://sourceforge.net/apps/trac/sourceforge/wiki/What%20is%20SourceForge.net
250K projects
http://en.wikipedia.org/wiki/Comparison_of_open_source_software_hosting_facilities
93K projects1 million users
200 projectshttp://projects.apache.org/indexes/alpha.html
661K projects29 billion of lines of codes3 million users
33K projects
http://www.ohloh.net/
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Mining
Data mining = “computational process of discovering patterns in large data sets”
2014 Marco Aurélio Gerosa ([email protected]) 3
http://2014.msrconf.org/
“The Mining Software Repositories (MSR) field analyzes the rich data available in software repositories to uncover interesting and actionable information about software systems and projects.”
Mining
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Programmers of a given language are happier than the others?
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Sentiment analysis on commits
2014 Marco Aurélio Gerosa ([email protected]) 5
http://geeksta.net/geeklog/exploring-expressions-emotions-github-commit-messages/
http://www.igvita.com/slides/2012/bigquery-github-strata.pdf
GitHub Data Challenge 2nd place
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If a programmer knows a language, which others does she know?
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Programming language relations
2014 Marco Aurélio Gerosa ([email protected]) 7
https://github.com/mjwillson/ProgLangVisualise
http://www.igvita.com/slides/2012/bigquery-github-strata.pdf
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When are the bugs inserted?
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Don’t program on Fridays
2014 Marco Aurélio Gerosa ([email protected]) 9
http://www.slideshare.net/taoxiease/software-analytics-towards-software-mining-that-matters
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Which files are more buggy?
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And what about social data?IS IT IMPORTANT FOR SOFTWARE ENGINEERING?
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David Parnas
2014 Marco Aurélio Gerosa ([email protected]) 13
“Software Engineering = Multi-person development of multi-version programs”David Parnas
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Frederick Brooks
2014 Marco Aurélio Gerosa ([email protected]) 14
Frederick Brooks
http://en.wikipedia.org/wiki/The_Mythical_Man-Month#Communication
“To avoid disaster, all the teams working on a project should remain in contact with each other in as many ways as possible”
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Conway’s law
2014 Marco Aurélio Gerosa ([email protected]) 15
“Organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations”
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Agile manifesto
2014 Marco Aurélio Gerosa ([email protected]) 16
“Individuals and interactions over processes and tools”
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Mining software repositories
2014 Marco Aurélio Gerosa ([email protected]) 18
Mining
Information about a project
Information about an ecosystem
Information about Software
Engineering
Decision making
Software understanding
Support maintenance
Empirical validation of ideas
& techniques
Collaboration and software production
Practitioner Researcher
Discussion listsComments on issuesCode commentsUser reportsQ&A sitesSocial media
Source code and artifacts
Issue trackersProject management systemsReputation systems
Applications
Tag cloud from MSR 2014 CFP
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Coordination requirements
2014 Marco Aurélio Gerosa ([email protected]) 19
Cataldo, M., Dependencies in geographically distributed software development: Overcoming the limits of modularity, PhD Thesis
Santana, F. et a. “XFlow: An Extensible Tool for Empirical Analysis of Software Systems Evolution”. ESELAW 2011
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Programmers who changed this function also changed …
2014 Marco Aurélio Gerosa ([email protected]) 20
Thomas Zimmermann, Peter Weissgerber, Stephan Diehl, and Andreas Zeller. 2005. Mining Version Histories to Guide Software Changes. IEEE Trans. Software Eng. 31, 6 (June 2005), 429-445. DOI=10.1109/TSE.2005.72 http://dx.doi.org/10.1109/TSE.2005.72
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Some of our work
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Change Coupling
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Change coupling Files frequently changed together share some sort of dependency [Gall et al. 1998]
The concept has proven useful in several different studies:◦ Software quality analysis [Cataldo & Nambiar, 2010]◦ Bugs prediction [D’Ambros et al. , 2009a]◦ Change prediction and change impact analysis [Zimmermann et al., 2005]◦ Uncover cross-cutting concerns [Adams et al., 2010]◦ Uncover design flaws and opportunities for refactoring [D’Ambros et al.,
2009b]◦ Understand and evaluate software architecture [Zimmermann et al., 2003]◦ Requirements traceability [Ali et al., 2013]◦ Maintain documentation [Kagdi et al., 2006]
2014 Marco Aurélio Gerosa ([email protected]) 23
Strong change dependency from B to A (the opposite is a much weaker
dependency)
A logical dependency denotes an implicit and evolutionary relationship between software artifacts
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1.1) Structural x Change coupling
2014 Marco Aurélio Gerosa ([email protected]) 24
Analysis of 150K commits of the ASF showed that:- 93% of the change dependencies did not involve structural dependencies- 95% of the structural dependencies did not imply in a change dependency
Oliva, G.A., Gerosa, M. A. (2011) “On the Interplay between Structural and Logical Dependencies in Free Software”. Brazilian Symposium on Software Engineering (SBES 2011)
Overlap between change dependencies and structural dependencies
Gustavo Oliva, PhD candidate
S t r u c t u r a lD e p e n d e n c y C o - c h a n g e s ?
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1.2) Change coupling originsManual classification of commits to understand the origins of change coupling
2014 Marco Aurélio Gerosa ([email protected]) 25
CategoryJoint-
changesTotal %
Refactoring elements that belong to a same semantic class
80 19.6%
Structural dependencies on a changing semantic class
9 2.2%
Cross-cutting concerns 165 40.4%Overloaded revision 60 14.7%
Repository operations 21 5.1%Structural dependencies on
specific elements66 16.2%
Other reasons 7 1.7%Total 408
Oliva, G.A., Santana, F., Gerosa, M. A., Souza, C. (2011) “Towards a Classification of Logical Dependencies Origins: A Case Study”. Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution (IWPSE-EVOL '11)
Gustavo Oliva, PhD candidate
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1.3) Preprocessing commits
2014 Marco Aurélio Gerosa ([email protected]) 26
Evaluation in the Apache code repository showed that the produced grouping corresponded to 4.6% of the number of commits
What about commit habits/practices/policies? Social aspects matter!
Oliva, G. A., Santana, F., Gerosa, M. A., Souza, C. (2012), “Preprocessing Change-Sets to Improve Logical Dependencies Identification”, 6th Int. Workshop on Software Quality and Maintainability (SQM 2012)
Commit = change?Using the sliding time window approach [Zimmermann & Weißgerber, 2004] to group SVN commits
Gustavo Oliva, PhD candidate
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How to identify design degradation?
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2) Design degradation identification
2014 Marco Aurélio Gerosa ([email protected]) 28
Rigidity and fragility [Martin & Martin, 2006] identification based on commit metadata
Oliva, G., Steinmacher, I., Wiese, I.S., Gerosa, M.A. “What Can Commit Metadata Tell Us About Design Degradation?”, In: 13th International Workshop on Principles on Software Evolution (IWPSE 2013), Saint Petersburg.
Gustavo Oliva, PhD candidate
5.3
Rigidity => designs difficult to change due to ripple effects => commit density (number of changed files per commit) Fragility => designs break in different areas when a change is performed => commit dispersion (distance in the directory tree among file paths included in a commit)
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Who are the key developers?
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3) Key developers characterization
2014 Marco Aurélio Gerosa ([email protected]) 30
Key developers participation
Oliva, G., Santana, F.W., da Silva, J. T., Oliveira, K.C.M., Werner, C.M.L., Souza, C.R.B. & Gerosa, M.A., “Evolving the System’s Core: A Case Study on the Identification and Characterization of Key Developers in Apache Ant”, Computing and Informatics [to appear].
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Do tests characteristics indicate the quality of the code under test?
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4) Unit tests’ feedback for code quality
2014 Marco Aurélio Gerosa ([email protected]) 32
Number of asserts indicate - Cyclomatic complexity?- LOC ?- Method calls ?
22 ASF projects 3 industry projects
- “Asserted Objects” metric presents better results than “number of asserts”- Statistically difference in 20% of the projects
Mauricio Aniche, PhD candidate
Aniche, M., Oliva, G.A., Gerosa, M.A., “What Do the Asserts in a Unit Test Tell Us about Code Quality? A Study on Open Source and Industrial Projects”, 17th European Conference on Software Maintenance and Reengineering (CSMR 2013).
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Does refactoring reduce cyclomatic complexity?
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5) Refactoring
2014 Marco Aurélio Gerosa ([email protected]) 34
Most part of the documented refactoring does not reduce cyclomatic complexity. However, 23% of the documented refactoring reduce cyclomatic complexity while 12% of the other commits have the same effect.
Sokol, F., Aniche, M.F., Gerosa, M.A., “Does the Act of Refactoring Really Make Code Simpler? A Preliminary Study”,. In: IV Brazilian Workshop of Agile Methods (WBMA 2013).
Francisco Sokol, BSc
www.metricminer.org.br
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Is it possible to predict changes, bugs, and change dependencies?
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6) Change prediction
2014 Marco Aurélio Gerosa ([email protected]) 36
Using social, process, and architectural metrics to improve change proneness prediction
13 projects, 6 classifiers, 11 metrics
Social and architectural metrics improved the prediction model
Similar results when considering projects grouped by change ratio
Igor Wiese, PhD candidate
Wiese, I. S, Nassif Jr, Steinmacher I, Re Reginaldo, Gerosa, M.A “Comparing communication and development networks for predicting file change proneness: An exploratory study considering process and social metrics”,. In: International Workshop on Software Quality and Maintainability (SQM 2014).
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Why do newcomers dropout from OSS projects?
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7) Supporting Newcomers to OSS projects
Analysis of newcomers dropout reasons
Use of MSR to identify newcomers
2014 Marco Aurélio Gerosa ([email protected]) 38
Steinmacher, I., Wiese, I.S., Chaves, A.P., Gerosa, M.A., Why do newcomers abandon open source software projects?, 6th Int. Workshop on Cooperative and Human Aspects of Software Engineering (CHASE 2013)
Igor Steinmacher, PhD candidate
Systematic review on awareness in DSD [JCSCW 2013]
C O M U N I C A Ç Ã O
C O O R D E N A Ç Ã OC O O P E R A Ç Ã O
g e r a c o m p r o m i s s o s g e r e n c i a d o s p e l a
o r g a n i z a a s t a r e f a s p a r a
d e m a n d aA w a r e n e s s
c o m u m + a ç ã oA ç ã o d e t o r n a r c o m u m
c o + o r d e m + a ç ã oA ç ã o d e o r g a n i z a r
e m c o n j u n t o
c o + o p e r a r + a ç ã oA ç ã o d e o p e r a r
e m c o n j u n t o
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How to use MSR to support newcomers?
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7) Supporting Newcomers to OSS projects◦ Systematic Literature Review on barriers for newcomers to OSS◦ Qualitative analysis of interviews
402014 Marco Aurélio Gerosa ([email protected])
Igor Steinmacher, PhD candidate
Steinmacher, I., Wiese, I., Conte, T., Gerosa, M.A., Redmiles, D.F. The Hard Life of Newcomers to OSS Projects, 7th International Workshop on Cooperative and Human Aspects of Software Engineering Steinmacher, I.; Graciotto Silva, M. A. ; GEROSA, M.A., Barriers faced by newcomers to open source projects: a systematic review. 10th International Conference on Open Source Systems (OSS 2014)
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2014 Marco Aurélio Gerosa ([email protected]) 41