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Bad smells in software analytics papers
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DOI：10.1016/j.infsof.2019.04.005.png)
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Abstract
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Context There has been a rapid growth in the use of data analytics to underpin evidence-based software engineering. However the combination of complex techniques, diverse reporting standards and poorly understood underlying phenomena are causing some concern as to the reliability of studies. Objective: Our goal is to provide guidance for producers and consumers of software analytics studies (computational experiments and correlation studies). Method: We propose using bad smells, i.e., surface indications of deeper problems and popular in the agile software community and consider how they may be manifest in software analytics studies. Results: We list 12 bad smells in software analytics papers (and show their impact by examples). Conclusions: We believe the metaphor of bad smell is a useful device. Therefore we encourage more debate on what contributes to the validity of software analytics studies (so we expect our list will mature over time).
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