Identifying cross-cutting concerns using software repository mining

Many software production processes advocate rigorous development testing alongside functional code writing, which implies that both test code and production code should co-evolve. To gain insight in the nature of this co-evolution, this paper proposes three views (realized by a tool called TeMo) that combine information from a software project's versioning system, the size of the various artifacts and the test coverage reports. We validate these views against two open source and one industrial software project and evaluate our results both with the help of log messages, code inspections and the original developers of the software system. With these views we could recognize different co-evolution scenarios (i.e., synchronous and phased) and make relevant observations for both developers as well as test engineers.

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“…In particular, Breu and Zimmermann (2006), Adams et al (2010) and Mulder and Zaidman (2010) have made efforts in this area.…”

Section: Related Workmentioning

confidence: 99%

Studying the co-evolution of production and test code in open source and industrial developer test processes through repository mining

et al. 2010

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“…If new code, which performs writes, is introduced as well in the same revision, analysis results can be confusing. This can be avoided by separately committing refactoring changes and new code (i.e., self‐contained commits ).…”

Section: Discussionmentioning

confidence: 99%

Detecting and analyzing I/O performance regressions

Bezemer

Milon

Zaidman

et al. 2014

J. Softw. Evol. and Proc.

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SUMMARY Regression testing can be performed by reexecuting a test suite on different software versions and comparing the outcome. For functional testing, the outcome of such tests is either pass (correct behavior) or fail (incorrect behavior). For nonfunctional testing, such as performance testing, this is more challenging as correct and incorrect are not clearly defined concepts for these types of testing. In this paper, we present an approach for detecting and analyzing input/output (I/O) performance regressions. Our method is supplemental to existing profilers, and its goal is to analyze the effect of source code changes on the performance of a system. In this paper, we focus on analyzing the amount of I/O writes being performed. The open source implementation of our approach, SPECTRAPERF, is available for download. We evaluate our approach in a field user study on Tribler, an open source peer‐to‐peer client and its decentralized solution for synchronizing messages, Dispersy. In this evaluation, we show that our approach can guide the performance optimization process, as it helps developers to find performance bottlenecks on the one hand and, on the other, allows them to validate the effect of performance optimizations. In addition, we perform a feasibility study on Django, the most popular Python project on Github, to demonstrate our applicability on other projects. Copyright © 2014 John Wiley & Sons, Ltd.

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“…• History Based (HB): This technique intends to discover crosscutting concerns by analyzing the changes made in the source code along the time by using software repositories like revision control systems, files and databases [21].…”

Section: Conducting the Systematic Reviewmentioning

confidence: 99%

A systematic review on mining techniques for crosscutting concerns

Durelli¹,

Santibanez

Anquetil

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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Background: The several maintenance tasks a system is submitted during its life usually cause its architecture deviates from the original conceivable design, ending up with scattered and tangled concerns across the software. The research area named concern mining attempts to identify such scattered and tangled concerns to support maintenance and reverse-engineering. Objectives: The aim of this paper is threefold: (i) identifying techniques employed in this research area, (ii) extending a taxonomy available on the literature and (iii) recommending an initial combination of some techniques. Results: We selected 62 papers by their mining technique. Among these papers, we identified 18 mining techniques for crosscutting concern. Based on these techniques, we have extended a taxonomy available in the literature, which can be used to position each new technique, and to compare it with the existing ones along relevant dimensions. As consequence, we present some combinations of these techniques taking into account high values of precision and recall that could improve the identification of both Persistence and Observer concerns. The combination that we recommend may serve as a roadmap to potential users of mining techniques for crosscutting concerns.

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Identifying cross-cutting concerns using software repository mining

Cited by 9 publications

References 32 publications

Studying the co-evolution of production and test code in open source and industrial developer test processes through repository mining

Studying the co-evolution of production and test code in open source and industrial developer test processes through repository mining

Detecting and analyzing I/O performance regressions

A systematic review on mining techniques for crosscutting concerns

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