CodeShovel: A Reusable and Available Tool for Extracting Source Code Histories

Grund, Felix; Chowdhury, Shaiful Alam; Bradley, N; Hall, Braxton; Holmes, Reid

doi:10.1109/icse-companion52605.2021.00100

Cited by 2 publications

(2 citation statements)

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“…To understand the relationship between method size and change-/bug-proneness, we need method-level evolution history. We used the state-of-the-art history tracing tool CodeShovel [28,29], which returns the complete change history of a given method. CodeShovel also collects information about when a method was modified, who…”

Section: Data Collectionmentioning

confidence: 99%

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An Empirical Study on Maintainable Method Size in Java

Chowdhury¹,

Uddin²,

Holmes³

2022

Preprint

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Code metrics have been widely used to estimate software maintenance effort. Metrics have generally been used to guide developer effort to reduce or avoid future maintenance burdens. Size is the simplest and most widely deployed metric. The size metric is pervasive because size correlates with many other common metrics (e.g., McCabe complexity, readability, etc.). Given the ease of computing a method's size, and the ubiquity of these metrics in industrial settings, it is surprising that no systematic study has been performed to provide developers with meaningful method size guidelines with respect to future maintenance effort. In this paper we examine the evolution of ∼785K Java methods and show that developers should strive to keep their Java methods under 24 lines in length. Additionally, we show that decomposing larger methods to smaller methods also decreases overall maintenance efforts. Taken together, these findings provide empirical guidelines to help developers design their systems in a way that can reduce future maintenance. CCS Concepts• Software engineering → Code metrics.

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Section: Data Collectionmentioning

confidence: 99%

“…Capturing bug related keywords from the commit messages for measuring bug-proneness is only ∼80% accurate [78]. Also, the CodeShovel tool [28] we used for capturing method history is accurate only for 90% of the methods [29].…”

Section: Threats To Validitymentioning

confidence: 99%

An Empirical Study on Maintainable Method Size in Java

Chowdhury¹,

Uddin²,

Holmes³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Revisiting the debate: Are code metrics useful for measuring maintenance effort?

et al. 2022

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Evaluating and predicting software maintenance effort using source code metrics is one of the holy grails of software engineering. Unfortunately, previous research has provided contradictory evidence in this regard. The debate is still open: as a community we are not certain about the relationship between code metrics and maintenance impact. In this study we investigate whether source code metrics can indeed establish maintenance effort at the previously unexplored method level granularity. We consider ∼730K Java methods originating from 47 popular open source projects. After considering seven popular method level code metrics and using change proneness as a maintenance effort indicator, we demonstrate why past studies contradict one another while examining the same data. We also show that evaluation context is king. Therefore, future research should step away from trying to devise generic maintenance models and should develop models that account for the maintenance indicator being used and the size of the methods being analyzed. Ultimately, we show that future source code metrics can be applied reliably and that these metrics can provide insight into maintenance effort when they are applied in a judiciously context-sensitive manner.

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CodeShovel: A Reusable and Available Tool for Extracting Source Code Histories

Cited by 2 publications

References 1 publication

An Empirical Study on Maintainable Method Size in Java

An Empirical Study on Maintainable Method Size in Java

Revisiting the debate: Are code metrics useful for measuring maintenance effort?

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