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Toward Predicting Architectural Significance of Implementation - - PowerPoint PPT Presentation

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MSR 2018 Toward Predicting Architectural Significance of Implementation Issues Arman Shahbazian, Daye Nam, and Nenad Medvidovic University of Southern California Mo Motivation Mo Motivation Mo Motivation B A C Mo Motivation B A C

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Toward Predicting Architectural Significance of Implementation Issues

Arman Shahbazian, Daye Nam, and Nenad Medvidovic University of Southern California

MSR 2018

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Mo Motivation

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Mo Motivation

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A

B

C

Mo Motivation

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A

B

C

Mo Motivation

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A

B

C

Mo Motivation

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Numerous Design Decisions Inadvertent Architectural Changes

A

B

C

Mo Motivation

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Numerous Design Decisions

  • Accumulation of Technical Debt
  • Deterioration of Software Quality

Inadvertent Architectural Changes

A

B

C

Mo Motivation

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A

B

C

Mo Motivation

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A

B

C

Architecturally Significant

Mo Motivation

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A

B

C

Architecturally Significant

Mo Motivation

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Da Dataset Cl Classifier De Detect ction

Con Contribution

  • ns
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Da Dataset Cl Classifier De Detect ction

Con Contribution

  • ns
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Da Dataset Cl Classifier De Detect ction

Con Contribution

  • ns
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Da Dataset Cl Classifier De Detect ction

Con Contribution

  • ns
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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

De Detectio tion + + Da Datas aset

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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

De Detectio tion + + Da Datas aset

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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

De Detectio tion + + Da Datas aset

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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

De Detectio tion + + Da Datas aset

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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

0.12.0 0.12.1

De Detectio tion + + Da Datas aset

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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

Recover

release-0.12.0 release-0.12.1

De Detectio tion + + Da Datas aset

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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

Before After

release-0.12.0 release-0.12.1

De Detectio tion + + Da Datas aset

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release-0.12.0 release-0.12.1

Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

Before After

Metric

De Detectio tion + + Da Datas aset

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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

Before After

release-0.12.0 release-0.12.1

Metric

De Detectio tion + + Da Datas aset

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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

De Detectio tion + + Da Datas aset

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Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

https://softarch.usc.edu/predictar

De Detectio tion + + Da Datas aset

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Feature Vectors Significant Issues Unsignificant Issues One-Hot Encoding TF (Term Frequencies) Non-Textual Contents Textual Contents

Classifier

Con Contribution

  • ns
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Precision Recall Hadoop 0.838 0.592 Nutch 0.946 0.247 Wicket 0.761 0.537 Cxf 0.865 0.538 OpenJpa 0.934 0.451 Cross-Project 0.811 0.583

Ev Evaluation

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Precision Recall Hadoop 0.838 0.592 Nutch 0.946 0.247 Wicket 0.761 0.537 Cxf 0.865 0.538 OpenJpa 0.934 0.451 Cross-Project 0.811 0.583

Ev Evaluation

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Precision Recall Hadoop 0.838 0.592 Nutch 0.946 0.247 Wicket 0.761 0.537 Cxf 0.865 0.538 OpenJpa 0.934 0.451 Cross-Project 0.811 0.583

Ev Evaluation

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Precision Recall Hadoop 0.838 0.592 Nutch 0.946 0.247 Wicket 0.761 0.537 Cxf 0.865 0.538 OpenJpa 0.934 0.451 Cross-Project 0.811 0.583

Ev Evaluation

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  • Automatically detecting architecturally significant issues,
  • A reusable dataset of over 21,000 issues,
  • Classifying them based on information contained in each issue.

Su Summar ary

  • Expand to more systems by adding the support for other issue trackers,
  • Improve the performance by adding more data,
  • Improve the performance by adapting new model in Machine Learning.

Fu Future Wo Work

Con Conclusion

  • n
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THANK YOU

(armansha@usc.edu, dayenam@usc.edu, neno@usc.edu)

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Data Collection

Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

  • ACDC: Algorithm for Comprehension-Driven Clustering
  • Structural pattern-based clustering
  • ARC: Architecture Recovery using Concerns
  • Concern-based hierarchical clustering based on similarity measure
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Evaluation

ARC ACDC Precision Recall Precision Recall Hadoop 0.793 0.637 0.883 0.547 Nutch 0.941 0.276 0.951 0.217 Wicket 0.843 0.657 0.678 0.417 Cxf 0.801 0.698 0.928 0.468 OpenJpa 0.965 0.503 0.903 0.399 Cross-Project 0.816 0.592 0.806 0.573

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Evaluation

ARC ACDC Precision Recall Precision Recall Hadoop 0.793 0.637 0.883 0.547 Nutch 0.941 0.276 0.951 0.217 Wicket 0.843 0.657 0.678 0.417 Cxf 0.801 0.698 0.928 0.468 OpenJpa 0.965 0.503 0.903 0.399 Cross-Project 0.816 0.592 0.806 0.573

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Evaluation

ARC ACDC Precision Recall Precision Recall Hadoop 0.793 0.637 0.883 0.547 Nutch 0.941 0.276 0.951 0.217 Wicket 0.843 0.657 0.678 0.417 Cxf 0.801 0.698 0.928 0.468 OpenJpa 0.965 0.503 0.903 0.399 Cross-Project 0.816 0.592 0.806 0.573

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Evaluation

ARC ACDC Precision Recall Precision Recall Hadoop 0.793 0.637 0.883 0.547 Nutch 0.941 0.276 0.951 0.217 Wicket 0.843 0.657 0.678 0.417 Cxf 0.801 0.698 0.928 0.468 OpenJpa 0.965 0.503 0.903 0.399 Cross-Project 0.816 0.592 0.806 0.573

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ARMAN SHAHBAZIAN, DAYE NAM, NENAD MEDVIDOVIC UNIVERSITY OF SOUTHERN CALIFORNIA

Toward Predicting Architectural Significance of Implementation Issues

MSR 2018

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Da Dataset

A Dataset of 21,062 Issues Identified Across 5 Large OSSs

De Detect ction

Automatic Detection of Architecturally Significant Issues

Cl Classifier

A Classifier Architectural Significance

  • f New Issue

Contributions

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Data Collection

Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

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Future Works

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Motivation

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Data Collection

Commit Analysis Architecture Recovery Change Detection For each issue: Significant Issues Issues After Before System Architectures

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Contributions Da Dataset

A Dataset of 21,062 Issues Identified Across 5 Large OSSs

De Detect ction

Automatic Detection of Architecturally Significant Issues

Cl Classifier

A Classifier Architectural Significance

  • f New Issue