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The impact of tangled code changes on defect prediction models

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Abstract

When interacting with source control management system, developers often commit unrelated or loosely related code changes in a single transaction. When analyzing version histories, such tangled changes will make all changes to all modules appear related, possibly compromising the resulting analyses through noise and bias. In an investigation of five open-source Java projects, we found between 7 % and 20 % of all bug fixes to consist of multiple tangled changes. Using a multi-predictor approach to untangle changes, we show that on average at least 16.6 % of all source files are incorrectly associated with bug reports. These incorrect bug file associations seem to not significantly impact models classifying source files to have at least one bug or no bugs. But our experiments show that untangling tangled code changes can result in more accurate regression bug prediction models when compared to models trained and tested on tangled bug datasets—in our experiments, the statistically significant accuracy improvements lies between 5 % and 200 %. We recommend better change organization to limit the impact of tangled changes.

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Notes

  1. These findings confirm results of earlier research presented by Kawrykow and Robillard (2011), Kawrykow (2011).

  2. Since it is undecidable whether a program will ever terminate under arbitrary conditions, we are, in general, also unable to decide whether two code changes may influence each other during a possible infinite program run.

  3. This ConfVoters is slightly penalized by the artificial blob generation strategy pack creating blobs by combining changes to files based on directory distance (see Subsection 4.2). However, we favored a more realistic distribution of changes over total fairness across all ConfVoters.

  4. Since we are analyzing artificially tangled change sets only, the file mapping error rate without untangling lies at 100 %. Having a error rate after untangling of 19 %, the result is a reduction rate of 100 %−19 %=81 %.

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Acknowledgments

Jeremias Rößler and Nadja Altabari provided constructive feedback on earlier versions of this work. We thank the reviewers for their constructive comments.

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Authors and Affiliations

  1. Microsoft Research, 21 Station Road, Cambridge, CB1 2DZ, UK

    Kim Herzig

  2. Saarland University, Campus E1.1, 66123, Saarbücken, Germany

    Sascha Just & Andreas Zeller

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  1. Kim Herzig
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  2. Sascha Just
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  3. Andreas Zeller
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Correspondence to Kim Herzig.

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Communicated by: Massimiliano Di Penta and Sung Kim

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Herzig, K., Just, S. & Zeller, A. The impact of tangled code changes on defect prediction models. Empir Software Eng 21, 303–336 (2016). https://doi.org/10.1007/s10664-015-9376-6

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