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Adoption of Software Testing in Open Source Projects A Preliminary Study on 50,000 Projects Pavneet Singh Kochhar, Tegawendé F. Bissyandé, David Lo, Lingxiao Jiang

Adoption of Software Testing in Open Source Projects - A Preliminary Study on 50,000 Projects

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Page 1: Adoption of Software Testing in Open Source Projects - A Preliminary Study on 50,000 Projects

Adoption of Software Testing in Open Source ProjectsA Preliminary Study on 50,000 Projects

Pavneet Singh Kochhar, Tegawendé F. Bissyandé, David Lo, Lingxiao Jiang

Page 2: Adoption of Software Testing in Open Source Projects - A Preliminary Study on 50,000 Projects

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Importance of Software Testing

Functionality -- Requirements

Debugging – Software complexity

Costs – $59 billions for inadequate testing

Is Software testing part of the process of open source project development?

Page 3: Adoption of Software Testing in Open Source Projects - A Preliminary Study on 50,000 Projects

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Objective & Contributions

Popularity of test cases

Presence of test cases – project characteristics

Influence of software development artifacts

Large Scale Study on 50,000 GitHub projects

Page 4: Adoption of Software Testing in Open Source Projects - A Preliminary Study on 50,000 Projects

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Dataset & Statistical computations

Lines of code Real LOC – By programming languages

Number of test cases Count of test files

Developer contributions Project team size

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RQ1– Popularity of Test Cases

Projects % of ProjectsWithout Test Cases 57.34%

With Test Cases 42.66%

90% of the projects < 100 test cases7% of the projects have >100 & < 500 cases2.57% of the projects >500 test cases

Distribution of Test Cases

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RQ1– Popularity of Test CasesLOC & Test Cases

Statistically significant medians

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RQ1– Popularity of Test CasesLOC & Test Cases

Positive correlation between #LOC and #Test Cases (ρ=0.334)

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RQ1– Popularity of Test CasesTest cases/LOC & LOC

Inverse correlation between #LOC and #Test Cases ρ=-0.665

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RQ1– Developers & Test CasesDevelopers and Test cases

Négative correlation between #LOC and #Test Cases ρ=-0.665

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RQ1– Developers & Test CasesDevelopers and Test cases

Weak correlation between #Developers and #Test Cases ρ=-0.207

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RQ1– Developers & Test CasesDevelopers and Test cases/developer

Inverse correlation between Team size and #Test Cases per developer ρ=-0.445

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Conclusion

Preliminary results of a large scale surveyAdoption of Test cases in Open Source projectsGitHub datasets

Findings:o Test cases are often present in larger projectso # of test cases per LOC decreases with increasing LOC.o The more developers, the more test caseso The more developers, the less ratio of test cases/developer

Future agenda:-- Exploration of the influence of more development artifacts- - Check with other open source datasets