On the criteria for prioritizing code anomalies to identify architectural problems

Vidal, Santiago; Guimarães, Everton; Oizumi, Willian; Garcia, Alessandro; Pace, Andrés Díaz; Marcos, Claudia

doi:10.1145/2851613.2851941

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…The relevance of our idea and concrete solution have been recognized by the software engineering community. We published our results in three international conferences [Oizumi et al 2016] (Qualis A1), [Vidal et al 2015], and [Vidal et al 2016]; a symposium [Oizumi et al 2014a]; an international journal ; and a workshop [Oizumi et al 2014b, Albuquerque et al 2014. Awards.…”

Section: Discussionmentioning

confidence: 99%

Synthesis of Code Anomalies: Revealing Design Problems in the Source Code

Oizumi¹,

Garcia²

2016

Anais Do XXIX Concurso De Teses E Dissertações (CTD 2016)

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Design problems affect most software projects and make their maintenance expensive and impeditive. Thus, the identification of potential design problems in the source code – which is very often the only available and upto-date artifact in a project – becomes essential in long-living software systems. This identification task is challenging as the reification of design problems in the source code tend to be scattered through several code elements. However, stateof-the-art techniques do not provide enough information to effectively help developers in this task. In this work, we address this challenge by proposing a new technique to support developers in revealing design problems. This technique synthesizes information about potential design problems, which are materialized in the implementation under the form of syntactic and semantic anomaly agglomerations. Our evaluation shows that the proposed synthesis technique helps to reveal more than 1200 design problems across 7 industry-strength systems, with a median precision of 71% and a median recall of 78%. The relevance of our work has been widely recognized by the software engineering community through 2 awards and 7 publications in international and national venues.

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Section: Discussionmentioning

confidence: 99%

Synthesis of Code Anomalies: Revealing Design Problems in the Source Code

Oizumi¹,

Garcia²

2016

Anais Do XXIX Concurso De Teses E Dissertações (CTD 2016)

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“…Vidal et al [123] developed a semi-automated approach that recommends a ranking of code smells based on (i) past component modifications (e.g., number of changes during the system history), (ii) important modifiability scenarios, and (iii) relevance of the kind of smell assigned by developers. In a follow-up work, the same authors introduced a new criteria for prioritizing groups of code anomalies as indicators of architectural problems in evolving systems [122].…”

Section: Code Smell Detection and Prioritizationmentioning

confidence: 99%

Improving change prediction models with code smell-related information

et al. 2019

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Code smells represent sub-optimal implementation choices applied by developers when evolving software systems. The nagative impact of code smells has been widely investigated in the past: besides developers' productivity and ability to comprehend source code, researchers empirically showed that the presence of code smells heavily impacts the change-proneness of the affected classes. On the basis of these findings, in this paper we conjecture that code smell-related information can be effectively exploited to improve the performance of change prediction models, i.e., models having as goal that of indicating to developers which classes are more likely to change in the future, so that they may apply preventive maintenance actions. Specifically, we exploit the so-called intensity index -a previously defined metric that captures the severity of a code smell-and evaluate its contribution when added as additional feature in the context of three state of the art change prediction models based on product, process, and developer-based features. We also compare the performance achieved by the proposed model with the one of an alternative technique that considers the previously defined antipattern metrics, namely a set of indicators computed considering the history of code smells in files. Our results report that (i) the prediction performance of the intensity-including

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“…This kind of smell is not necessarily a design problem. In previous works, we analyzed the problem of prioritizing code smells based on multiple criteria [45][46][47][48]. A recent study [39] has also discussed reasons for not refactoring complex classes, and those reasons also apply to BMs.…”

Section: :5mentioning

confidence: 99%

Assessing the Refactoring of Brain Methods

Vidal

Berra

Zulliani

et al. 2018

ACM Trans. Softw. Eng. Methodol.

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Code smells are a popular mechanism for identifying structural design problems in software systems. Several tools have emerged to support the detection of code smells and propose some refactorings. However, existing tools do not guarantee that a smell will be automatically fixed by means of refactorings. This article presents Bandago, an automated approach to fix a specific type of code smell called Brain Method . A Brain Method centralizes the intelligence of a class and manifests itself as a long and complex method that is difficult to understand and maintain by developers. For each Brain Method , Bandago recommends several refactoring solutions to remove the smell using a search strategy based on simulated annealing. Our approach has been evaluated with several open-source Java applications, and the results show that Bandago can automatically fix more than 60% of Brain Methods . Furthermore, we conducted a survey with 35 industrial developers that showed evidence about the usefulness of the refactorings proposed by Bandago. Also, we compared the performance of the Bandago against that of a third-party refactoring tool.

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On the criteria for prioritizing code anomalies to identify architectural problems

Cited by 8 publications

References 6 publications

Synthesis of Code Anomalies: Revealing Design Problems in the Source Code

Synthesis of Code Anomalies: Revealing Design Problems in the Source Code

Improving change prediction models with code smell-related information

Assessing the Refactoring of Brain Methods

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