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Volatility metric to detect anomalies in source code repositories

Published: 17 October 2021 Publication History

Abstract

A new metric was introduced to calculate the distance between actively modified files in a source code repository and the files, which are rarely modified and may be considered abandoned or even dead. It was empirically demonstrated that larger repositories have larger values of the introduced metric. The metric may be used for earlier detection of code maintenance anomalies and helping software developers make the decision of splitting the repository into smaller ones in order to prevent maintainability issues.

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References

[1]
Natércia A Batista, Guilherme A Sousa, Michele A Brandão, Ana Paula C da Silva, and Mirella Moura Moro. 2018. Tie strength metrics to rank pairs of developers from github. Journal of Information and Data Management, 9, 1 (2018), 69–69.
[2]
Marco Biazzini and Benoit Baudry. 2014. “May the fork be with you”: novel metrics to analyze collaboration on GitHub. In Proceedings of the 5th International Workshop on Emerging Trends in Software Metrics. 37–43.
[3]
Garvit Rajesh Choudhary, Sandeep Kumar, Kuldeep Kumar, Alok Mishra, and Cagatay Catal. 2018. Empirical analysis of change metrics for software fault prediction. Computers & Electrical Engineering, 67 (2018), 15–24.
[4]
Serge Demeyer, Stéphane Ducasse, and Oscar Nierstrasz. 2000. Finding refactorings via change metrics. ACM SIGPLAN Notices, 35, 10 (2000), 166–177.
[5]
NE Fenton and SL Pfleeger. 1997. Software Metrics.
[6]
Francesca Arcelli Fontana, Matteo Rolla, and Marco Zanoni. 2014. Capturing Software Evolution and Change through Code Repository Smells. In International Conference on Agile Software Development. 148–165.
[7]
Ilja Heitlager, Tobias Kuipers, and Joost Visser. 2007. A practical model for measuring maintainability. In 6th international conference on the quality of information and communications technology (QUATIC 2007). 30–39.
[8]
Jez Humble and David Farley. 2010. Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation (Adobe Reader). Pearson Education.
[9]
Ciera Jaspan, Matthew Jorde, Andrea Knight, Caitlin Sadowski, Edward K Smith, Collin Winter, and Emerson Murphy-Hill. 2018. Advantages and disadvantages of a monolithic repository: a case study at google. In Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice. 225–234.
[10]
Jon Loeliger and Matthew McCullough. 2012. Version Control with Git: Powerful tools and techniques for collaborative software development. O’Reilly Media, Inc.
[11]
Andrew Meneely and Oluyinka Williams. 2012. Interactive churn metrics: socio-technical variants of code churn. ACM SIGSOFT Software Engineering Notes, 37, 6 (2012), 1–6.
[12]
Raimund Moser, Witold Pedrycz, and Giancarlo Succi. 2008. A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction. In Proceedings of the 30th international conference on Software engineering. 181–190.
[13]
John C Munson and Sebastian G Elbaum. 1998. Code Churn: A measure for estimating the impact of code change. In Proceedings. International Conference on Software Maintenance (Cat. No. 98CB36272). 24–31.
[14]
K Muthukumaran, Abhinav Choudhary, and NL Bhanu Murthy. 2015. Mining GitHub for novel change metrics to predict buggy files in software systems. In 2015 International Conference on Computational Intelligence and Networks. 15–20.
[15]
Yonghee Shin, Andrew Meneely, Laurie Williams, and Jason A Osborne. 2010. Evaluating complexity, code churn, and developer activity metrics as indicators of software vulnerabilities. IEEE transactions on software engineering, 37, 6 (2010), 772–787.

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  1. Volatility metric to detect anomalies in source code repositories

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    cover image ACM Conferences
    BCNC 2021: Proceedings of the 1st ACM SIGPLAN International Workshop on Beyond Code: No Code
    October 2021
    35 pages
    ISBN:9781450391252
    DOI:10.1145/3486949
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 17 October 2021

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    Author Tags

    1. Metrics
    2. Software Maintainability
    3. Software Size

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    SPLASH '21
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    SPLASH '21: Software for Humanity
    October 17, 2021
    IL, Chicago, USA

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