mining sociotechnical information from software repositories
DESCRIPTION
A large amount of data is produced during collaborative software development. The analysis of such data sets a great opportunity to better understand Software Engineering from the perspective of evidence-based research. Mining software repositories studies have explored both the technical and social aspects of software development contributed to the discovery of important information about how software development evolves and how developers collaborate. Several repositories store data regarding source code production (version control systems), communication between developers and users (forums and mailing lists), and coordination of activities (issue tracker, task managers, etc.). In the open source world, such data is available in large ecosystems of software development. Platforms such as GitHub host millions of repositories, which receive contributions from millions of developers worldwide. Some project repositories register data from more than a decade of development, enabling the analysis of projects from a historical perspective. In this talk, I will discuss some of the uses and challenges of mining software repositories, focusing on some works conducted in our group, such as: identification of change dependencies, evaluation of architectural degradation from commit meta-data, core-periphery analysis of developers participation, change-proneness prediction, analysis of the impact of refactoring on code quality, and relations between quality attributes of the test and the code being tested.TRANSCRIPT
Mining Sociotechnical Information From Software
Repositories
UNIVERSITY OF SÃO PAULO, BRAZIL
University of California, IrvineInformatics Seminar
March/2014
Marco Aurélio Gerosa
Marco Aurélio Gerosa ([email protected]) 2
Software repositories
March/2014
Programmer
Tester
UserClient
Programmer
ManagerSW Architect
Source Code
MessageArchives
BugReportsIssues Etc.
Current and historical artifacts and interactions are registered in software repositories
Computer Mediated Tools
Marco Aurélio Gerosa ([email protected]) 3
Repositories of repositories
March/2014
https://github.com/about/presshttp://octoverse.github.com/
11.3 millions repositories5.4 millions usersIn 2013:• 3 millions new users• 152 millions pushes• 25 millions comments• 14 millions issue• 7 millions pull requests
36K projectshttp://en.wikipedia.org/wiki/CodePlex
30K projectshttps://launchpad.net
324K projects3.4 millions developershttp://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 billions of lines of codes3 millions users
33K projects
http://www.ohloh.net/
Marco Aurélio Gerosa ([email protected]) 4
Mining software repositories
March/2014
Mining
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.”
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
*3C Model: Fuks, H., Raposo, A., Gerosa, M.A., Pimentel, M. & Lucena, C.J.P. (2007) “The 3C Collaboration Model” in: The Encyclopedia of E-Collaboration, Ned Kock (org), ISBN 978-1-59904-000-4, pp. 637-644.
Com m unica tion
Coordina tionCoope ra tion
3C M odel*
Discussion listsComments on issuesCode commentsUser reportsQ&A sitesSocial mediaSource code
and artifacts Issue trackersProject management systemsReputation systems
Applications
Tag cloud from MSR 2014 CFP
Examples
Marco Aurélio Gerosa ([email protected]) 6
Sentiment analysis on commits
March/2014
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
Marco Aurélio Gerosa ([email protected]) 7
Programming language relations
March/2014
https://github.com/mjwillson/ProgLangVisualise
http://www.igvita.com/slides/2012/bigquery-github-strata.pdf
Marco Aurélio Gerosa ([email protected]) 8
Mining discussion lists
March/2014
Christian Bird, Alex Gourley, Prem Devanbu, Michael Gertz, and Anand Swaminathan. 2006. Mining email social networks. MSR 2006
Anja Guzzi, Alberto Bacchelli, Michele Lanza, Martin Pinzger, and Arie van Deursen. 2013. Communication in open source software development mailing lists. MSR 2013
What is the discussion list for?
Marco Aurélio Gerosa ([email protected]) 9
Coordination requirements
March/2014
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
Marco Aurélio Gerosa ([email protected]) 10
Programmers who changed this function also changed …
March/2014
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
Marco Aurélio Gerosa ([email protected]) 11
Don’t program on Fridays
March/2014
http://www.slideshare.net/taoxiease/software-analytics-towards-software-mining-that-matters
Marco Aurélio Gerosa ([email protected]) 13
Lack of documentation
March/2014
Deficient Documentation Detection: A Methodology to Locate Deficient Project Documentation using Topic Analysis, Joshua Charles Campbell, Chenlei Zhang, Zhen Xu, Abram Hindle, and James Miller, MSR 2013
Marco Aurélio Gerosa ([email protected]) 14
Some questions from MSR 2013• How to recommend developers to bug reports?
• How to filter update notifications?
• What can apps’ permissions reveal?
• How to extract feature requests from apps online reviews?
• How to analyze peer code-review data?
• How to find API usage patterns?
• Is programming knowledge related to age?
• How do patches reach the Linux kernel?
• How do code clones evolve?
• How to process crash reports?
• How to predict defects and effort?
• Etc.March/2014
http://2013.msrconf.org/program.php
Some of our work
Marco Aurélio Gerosa ([email protected]) 16
Some of our work1. Change dependencies identification
2. Design degradation identification
3. Key developers characterization
4. Unit tests’ feedback for code quality
5. Refactoring
6. Change prediction
7. Support for OSS projects newcomers
March/2014
Marco Aurélio Gerosa ([email protected]) 17
1) Change dependencies identification Dependency?
• “A dependency means that a client element has knowledge of the supplier element and a change in the supplier may affect the client” [Larman, 2004]
• A dependency relation means that the semantics of the depending elements is semantically or structurally dependent on the definition of the supplier element [UML Formal Specification]
• Unrecognized dependencies result in a higher number of defects [Herbsleb et al., 2006]
• Structural (or static), dynamic, or logical dependency?
• Dependencies may be hard to identify• Publisher/subscriber, polymorphism, clones, crosscutting concerns, semantic relations etc.
March/2014
A BC l as s A C l as s Bd o T h isF o rM e()
D ep en d en cy C h an g e
Marco Aurélio Gerosa ([email protected]) 18
Change dependencies 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]
March/2014
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
Marco Aurélio Gerosa ([email protected]) 19
1.1) Structural x Change Dependencies
March/2014
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 truc tura lD e pe nde nc y C o-ch an g es ?
Marco Aurélio Gerosa ([email protected]) 20
1.2) Change dependencies originsManual classification of commits to understand the origins of change dependencies
March/2014
Category Joint-changes Total %Refactoring elements that belong to
a same semantic class80 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
Marco Aurélio Gerosa ([email protected]) 21
1.3) Preprocessing commits
March/2014
N Sum Mean StDev Skewness Kurtosis
Before 479,794 3,206,900 6.68 37.84 33.80 1,844.00
After 453,865 3,174,051 6.99 40.79 39.56 2,829.94
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
Marco Aurélio Gerosa ([email protected]) 22
2) Design degradation identification
March/2014
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)
Marco Aurélio Gerosa ([email protected]) 23
3) Key developers characterization
March/2014
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].
Marco Aurélio Gerosa ([email protected]) 24
4) Unit tests’ feedback for code quality
March/2014
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).
Marco Aurélio Gerosa ([email protected]) 25
5) Refactoring
March/2014
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, MSc candidate
www.metricminer.org.br
Marco Aurélio Gerosa ([email protected]) 26
6) Change prediction
March/2014
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
Dimensions Metric Name Reference
Process
HCM Hassan [13] WHCM D’Ambros et al. [8] Churn D’Ambros et al. [8]
WChurn Nagappan and Ball [23]
Social
MFD – Modification File-Developer
New
DevParticipation Rahman & Devanbu [28] WDevParticipation New
Architecture
Rigidity Oliva et al. [26] Fragility Oliva et al. [26]
WCo-Changes New UD – Unworked
Dependencies New
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).
Marco Aurélio Gerosa ([email protected]) 27
7) Supporting Newcomers to OSS projects
Analysis of newcomers dropout reasons
Use of MSR to identify newcomers
March/2014
COMUNICAÇÃO
COORDENAÇÃOCOOPERAÇÃO
gera com prom issos gerenciados pela
organiza as tarefas para
dem andaAwareness
com um + açãoAção de tornar com um
co + ordem + açãoAção de organizar
em conjunto
co + operar + açãoAção de operar
em conjunto
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]
Marco Aurélio Gerosa ([email protected]) 28
7) Supporting Newcomers to OSS projects◦ Systematic Literature Review on barriers for newcomers to OSS◦ Qualitative analysis of interviews
March/2014
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)
And now?
Marco Aurélio Gerosa ([email protected]) 30
Some MSR challenges• Years of sociotechnical data of millions of projects• BigData & large-scale computing
• Natural Language processing• Hao Zhong, H., Zhang, L., Xie, T. & Mei, H. “Inferring Resource Specifications from Natural
Language API Documentation”, ASE 2009.
• Incomplete and inaccurate information (e.g., commit comments)
• Multiple identities and commits on behalf of others
• Traceability and provenance• Davies, German, Godfrey & Hindle. “Software bertillonage: finding the provenance of an entity”,
MSR 2011
• Tools usage change over time• Guzzi et al. “Communication in open source software development mailing lists”, MSR 2013
March/2014
Marco Aurélio Gerosa ([email protected]) 31
What’s next? (1/2) Towards theories and consolidation
Sharing insights
Sharing patterns ◦ DAPSE http://dapse.unbox.org ◦ MSR Cookbook
http://swag.cs.uwaterloo.ca/~okononen/msr /
Sharing data◦ http://www.githubarchive.org/◦ http://www.ohloh.net / ◦ Dataset papers published at MSR◦ MSR Challenges dataset◦ Promisedata.org Promisedata.org◦ Flossmetrics.org◦ Flossmole.org◦ MetricMiner
Replicating studies◦ Ghezzi, G. & Gall, H. “Replicating Mining Studies
with SOFAS”, MSR 2013 March/2014
http://thomas-zimmermann.com/2013/10/software-analytics-sharing-information/
[Gaines, 1999]
Marco Aurélio Gerosa ([email protected]) 32
What’s next? (2/2) Actionable information in the Software Environments (new IDEs)
Software Analytics◦ “The use of analysis, data, and systematic
reasoning to make decisions”
March/2014
http://thomas-zimmermann.com/2013/10/software-analytics-sharing-information/
New sources◦ A lot of information is lost between commits => IDE &
desktop instrumentation and live data collection ◦ Singer, J. “Navtracks: Supporting navigation in
software maintenance”, ICSM'05◦ Blincoe, K., Valetto, G. & Goggins, S. “Proximity: a
measure to quantify the need for developers' coordination. CSCW'12
◦ Software execution logs◦ Social Media
◦ Storey, M., Treude, C., Deursen, A. & Cheng, L., “The impact of social media on software engineering practices and tools” FoSER'10
◦ Bougie, G., Starke, J., Storey, M. & German, D., “Towards understanding twitter use in software engineering: preliminary findings, ongoing challenges and future questions”, Web2SE'11
◦ Q&A Sites◦ “Over 92% of Stack Overflow questions about expert
topics are answered - in a median time of 11 minutes” [Mamykina et al., CHI'11]
◦ Campbell et al., “Deficient Documentation Detection: A Methodology to Locate Deficient Project Documentation using Topic Analysis”, MSR'13
Marco Aurélio Gerosa ([email protected]) 33
Conferences◦ MSR: Working Conference on Mining Software Repositories
◦ http://msrconf.org◦ ICSM: International Conference on Software Maintenance
◦ http://conferences.computer.org/icsm◦ CSMR-WCRE: Software Evolution Week (joins CSMR and WCRE)
◦ http://ansymo.ua.ac.be/csmr-wcre◦ ICSE: International Conference on Software Engineering
◦ http://www.icse-conferences.org◦ SCAM: International Working Conference on Source Code Analysis and Manipulation
◦ http://www.ieee-scam.org/◦ ESEM: International Symposium on Empirical Software Engineering and Measurement
◦ http://www.esem-conferences.org◦ PROMISE
March/2014
MARCO AUREL IO GEROS A
(GEROS [email protected]) – OFF ICE 5228@DBH
@GEROS A_MARCO
HTTP: / / L APESSC. IME .USP.BR/ (ALL PUBL ICAT IONS ARE AVA IL ABLE AT TH IS S ITE )
HTTP : / /WWW. IME .USP.BR/~GEROS A
HTTP : / /NAP.USP.BR/NAWEB
Thank you!
Marco Aurélio Gerosa ([email protected]) 35
Other projects
March/2014
http://www.arquigrafia.org.br
Smart Audio City Guide
82,000 images of Brazilian architecture
A social mobile system based on geo-referenced audio information to support the mobility of blind people
Marco Aurélio Gerosa ([email protected]) 36
Opportunities for Research in Brazil More than 38k scholarships implemented in 2013 for sending students abroad
Visiting researcher (PVE Ciencia sem Fronteiras) - http://bit.ly/1fJPfLG ◦ Projects for 2 or 3 years, with a stay of 30 to 90 days per year in Brazil, continuous or not ◦ ≈ US$ 6400 (R$ 14,000)/month stayed in Brazil◦ ≈ US$ 23,000 (R$ 50,000)/year for funding + scholarships◦ Transportation assistance, among other benefits
Young Talent Attraction (BJT) - http://bit.ly/17T8ycr ◦ 1 to 3 years◦ Min ≈ US$ 3200 (R$ 50,000)/month◦ Min ≈ US$ 9200 (R$ 20,000)/year for funding◦ Transportation assistance, support for accommodation
Visiting scholar (CAPES PVE) http://capes.gov.br/cooperacao-internacional/multinacional/pve ◦ Visits from 15 days to 1 year◦ ≈ US$ 3200 or US$ 4100 (R$ 6900 or R$ 8900)/month depending on the level ◦ Support for accommodation and transportation
FAPESP (Sao Paulo state) - http://fapesp.br/147 ◦ Visiting professor: ≈US$ 6300, 5000, or 4200 (R$ 13,653, R$ 10,950, R$ 9184)/month
depending on the level, visits of any length up to 12 months + support for transportation and health insurance
◦ Post-doc: ≈US$ 2700 (R$ 5908) + support for accommodation. 6 to 36 months. USP (University of Sao Paulo) - http://www.usp.br/prp/pagina.php?menu=3&pagina=17
◦ Specific grants for receiving retired researchers or faculty on sabbatical Bilateral agreements (with several universities)
March/2014
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
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2012
0500
1,0001,5002,000
CNPq Investment on Research (in BRL mil-
lions)
1996
1998
2000
2002
2004
2006
2008
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2012
0
50,000
100,000
150,000
200,000
250,000
Scholarships abroad (in thousands BRL)
Marco Aurélio Gerosa ([email protected]) 37
References Change Dependencies
[Larman, 2004] LARMAN, CRAIG: Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development. third. ed. : Prentice Hall, 2004
[UML Formal Specification] OMG: Object Management Group: Unified Modeling Language (UML). http://www.omg.org/spec/UML/
[Ball et al, 1997] BALL, THOMAS ; ADAM, JUNG-MIN KIM ; HARVEY, A. PORTER ; SIY, P.: If Your Version Control System Could Talk... In: ICSE Workshop on Process Modeling and Empirical Studies of Software Engineering, 1997
[Gall et al., 1998] GALL, HARALD ; HAJEK, KARIN ; JAZAYERI, MEHDI: Detection of Logical Coupling Based on Product Release History. In: Proceedings of the International Conference on Software Maintenance, ICSM ’98. Washington, DC, USA : IEEE Computer Society, 1998 — ISBN 0-8186-8779-7, p. 190–
Change Dependencies and Its Use
[Cataldo & Nambiar, 2010] CATALDO, MARCELO ; NAMBIAR, SANGEETH: The impact of geographic distribution and the nature of technical coupling on the quality of global software development projects. In: Journal of Software Maintenance and Evolution: Research and Practice, John Wiley & Sons, Ltd. (2010)
[D’Ambros et al., 2009a] D’AMBROS, MARCO ; LANZA, MICHELE ; ROBBES, ROMAIN: On the Relationship Between Change Coupling and Software Defects. In: ZAIDMAN, A. ; ANTONIOL, G. ; DUCASSE, S. (eds.): 16th Working Conference on Reverse Engineering, WCRE 2009, 13-16 October 2009, Lille, France : IEEE Computer Society, 2009 — ISBN 978-0-7695-3867-9, pp. 135–144
[Zimmermann et al., 2005] ZIMMERMANN, THOMAS ; WEISSGERBER, PETER ; DIEHL, STEPHAN ; ZELLER, ANDREAS: Mining Version Histories to Guide Software Changes. In: IEEE Trans. Softw. Eng. vol. 31. Piscataway, NJ, USA, IEEE Press (2005), Nr. 6, pp. 429–445
March/2014
Marco Aurélio Gerosa ([email protected]) 38
References Change dependencies and its use (continued)
[Adams et al., 2010] ADAMS, BRAM ; JIANG, ZHEN MING ; HASSAN, AHMED E.: Identifying crosscutting concerns using historical code changes. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1, ICSE ’10. Cape Town, South Africa : ACM, 2010 — ISBN 978-1-60558-719-6, pp. 305–314
[D’Ambros et al., 2009b] D’AMBROS, MARCO ; LANZA, MICHELE ; LUNGU, MIRCEA: Visualizing Co-Change Information with the Evolution Radar. In: IEEE Trans. Software Eng vol. 35 (2009), Nr. 5, pp. 720–735
[Zimmermann et al., 2003] ZIMMERMANN, T. ; DIEHL, S. ; ZELLER, A.: How history justifies system architecture (or not). In: Software Evolution, 2003. Proceedings. Sixth International Workshop on Principles of, 2003, pp. 73–83
[Ali et al., 2013] ALI, N. ; JAAFAR, F. ; HASSAN, A.E.: Leveraging historical co-change information for requirements traceability. In: Reverse Engineering (WCRE), 2013 20th Working Conference on, 2013, pp. 361–370
[Kagdi et al., 2006] KAGDI, H. ; MALETIC, J.I.: Mining for Co-Changes in the Context of Web Localization. In: Web Site Evolution, 2006. WSE ’06. Eighth IEEE International Symposium on, 2006, pp. 50–57
Improving change dependencies identification
[Zimmermann et al., 2004] ZIMMERMANN, THOMAS ; WEIẞGERBER, PETER: Preprocessing CVS Data for Fine-Grained Analysis. In: Proceedings 1st International Workshop on Mining Software Repositories (MSR 2004). Los Alamitos CA : IEEE Computer Society Press, 2004, pp. 2–6
Design Degradation
[Martin & Martin, 2006] MARTIN, ROBERT C. ; MARTIN, MICAH: Agile Principles, Patterns, and Practices in C#. first. ed. : Prentice Hall, 2006
March/2014