Study on the relevance of the warnings reported by Java bug-finding tools

Araújo, João; Souza, Silvio Pinheiro de; Valente, M.

doi:10.1049/iet-sen.2009.0083

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…A bivariate auto-regressive model includes past values from the independent variable x and from 1 It is worth mentioning that we adopted this strategy in the two studies presented in this paper To apply Granger, we must first calculate the following bivariate auto-regressive model [4]:…”

Section: Granger Testmentioning

confidence: 99%

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Predicting software defects with causality tests

Couto

Pires²,

Valente³

et al. 2014

Journal of Systems and Software

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In this paper, we propose a defect prediction approach centered on more robust evidences towards causality between source code metrics (as predictors) and the occurrence of defects. More specifically, we rely on the Granger Causality Test to evaluate whether past variations in source code metrics values can be used to forecast changes in a time series of defects. Our approach triggers alarms when changes made to the source code of a target system have a high chance of producing defects. We evaluated our approach in several life stages of four Java-based systems. We reached an average precision of 50% in three out of the four systems we evaluated. Moreover, by comparing our approach with baselines that are not based on causality tests, it achieved a better precision.

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Section: Granger Testmentioning

confidence: 99%

“…Essentially, such techniques rely on different predictors, including source code metrics (e.g., coupling, cohesion, size) [2,25,31], change metrics [17], static analysis tools [1,6,24], and code smells [8].…”

Section: Introductionmentioning

confidence: 99%

Predicting software defects with causality tests

Couto

Pires²,

Valente³

et al. 2014

Journal of Systems and Software

View full text Add to dashboard Cite

show abstract

“…These precision values are similar to the ones generated for example by FindBugs [11] and PMD [12], which are well-known bug finding tools based on static analysis. For example, in a previous study, using as target five stable releases of the Eclipse platform, we found that precision rates superior to 50% are only possible by restricting the analysis to a small subset of the warnings raised by FindBugs (basically, high priority warnings from the correctness category) [14]. For PMD, the precision was inferior to 10%.…”

Section: Combining the Heuristicsmentioning

confidence: 99%

“…For example, in a previous study, using as target five stable releases of the Eclipse platform, we measured the precision of the warnings raised by two Java-based bug finding tools [14]. We defined precision by the following ratio: (#warnings removed after a given time frame) / (#warnings issued by the tool).…”

Section: A Static Analysis Toolsmentioning

confidence: 99%

Heuristics for discovering architectural violations

Maffort¹,

Valente²,

Bigonha³

et al. 2013

2013 20th Working Conference on Reverse Engineering (WCRE)

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Abstract-Software architecture conformance is a key software quality control activity that aims to reveal the progressive gap normally observed between concrete and planned software architectures. In this paper, we present ArchLint, a lightweight approach for architecture conformance based on a combination of static and historical source code analysis. For this purpose, ArchLint relies on four heuristics for detecting both absences and divergences in source code based architectures. We applied ArchLint in an industrial-strength system and as a result we detected 119 architectural violations, with an overall precision of 46.7% and a recall of 96.2%, for divergences. We also evaluated ArchLint with four open-source systems, used in an independent study on reflexion models. In this second study, ArchLint achieved precision results ranging from 57.1% to 89.4%.

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“…Essentially, such techniques rely on different predictors, including source code metrics (e.g. coupling, cohesion, size) [3], [22], [24], change metrics [15], static analysis tools [2], [21], and code smells [7]. However, the typical experiments designed to evaluate bug prediction techniques usually do not investigate whether the discovered relationships indicate a cause-effect relation or whether they are mere statistical coincidences.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Causal Relationships between Software Metrics and Bugs

Couto

Silva

Valente³

et al. 2012

2012 16th European Conference on Software Maintenance and Reengineering

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Abstract-Bug prediction is an important challenge for software engineering research. It consist in looking for possible early indicators of the presence of bugs in a software. However, despite the relevance of the issue, most experiments designed to evaluate bug prediction only investigate whether there is a linear relation between the predictor and the presence of bugs. However, it is well known that standard regression models can not filter out spurious relations. Therefore, in this paper we describe an experiment to discover more robust evidences towards causality between software metrics (as predictors) and the occurrence of bugs. For this purpose, we have relied on Granger Causality Test to evaluate whether past changes in a given time series are useful to forecast changes in another series. As its name suggests, Granger Test is a better indication of causality between two variables. We present and discuss the results of experiments on four real world systems evaluated over a time frame of almost four years. Particularly, we have been able to discover in the history of metrics the causes -in the terms of the Granger Test -for 64% to 93% of the defects reported for the systems considered in our experiment.

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Study on the relevance of the warnings reported by Java bug-finding tools

Cited by 17 publications

References 14 publications

Predicting software defects with causality tests

Predicting software defects with causality tests

Heuristics for discovering architectural violations

Uncovering Causal Relationships between Software Metrics and Bugs

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