Detecting code smells using machine learning techniques: Are we there yet?

Published: March 1, 2018

Code smells are symptoms of poor design and implementation choices weighing heavily on the quality produced source code. During last decades several code smell detection tools have been proposed. However, literature shows that results these can be subjective intrinsically tied to nature approach detection. In a recent work use Machine-Learning (ML) techniques for has proposed, possibly solving issue tool subjectivity giving learner ability discern between smelly non-smelly elements. While this opened new perspective detection, it only considered case where instances affected by single type contained in each dataset used train test machine learners. we replicate study with different configuration containing more than one smell. The reveal learning critical limitations state art which deserve further research.

Language: Английский

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Machine learning techniques for code smell detection: A systematic literature review and meta-analysis

Information and Software Technology, Journal Year: 2019, Volume and Issue: 108, P. 115 - 138

Published: Jan. 4, 2019

Language: Английский

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Comparing Heuristic and Machine Learning Approaches for Metric-Based Code Smell Detection

Published: May 1, 2019

Code smells represent poor implementation choices performed by developers when enhancing source code. Their negative impact on code maintainability and comprehensibility has been widely shown in the past several techniques to automatically detect them have devised. Most of these are based heuristics, namely they compute a set metrics combine creating detection rules; while reasonable accuracy, recent trend is represented use machine learning where used as predictors smelliness artefacts. Despite advances field, there still noticeable lack knowledge whether can actually be more accurate than traditional heuristic-based approaches. To fill this gap, paper we propose large-scale study empirically compare performance machine-learning-based for metric-based smell detection. We consider five types models with DECOR, state-of-the-art approach. Key findings emphasize need further research aimed at improving effectiveness both heuristic approaches detection: DECOR generally achieves better baseline, its precision too low make it usable practice.

Language: Английский

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A survey of many-objective optimisation in search-based software engineering

Journal of Systems and Software, Journal Year: 2018, Volume and Issue: 149, P. 382 - 395

Published: Dec. 18, 2018

Language: Английский

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The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study

IEEE Transactions on Software Engineering, Journal Year: 2022, Volume and Issue: 49(1), P. 44 - 63

Published: Jan. 6, 2022

Software vulnerabilities are weaknesses in source code that can be potentially exploited to cause loss or harm. While researchers have been devising a number of methods deal with vulnerabilities, there is still noticeable lack knowledge on their software engineering life cycle, for example how introduced and removed by developers. This information design more effective vulnerability prevention detection, as well understand the granularity at which these should aim. To investigate cycle known we focus how, when, under circumstances contributions introduction projects made, long, they xmlns:xlink="http://www.w3.org/1999/xlink">removed . We consider 3,663 public patches from National Vulnerability Database—pertaining 1,096 open-source GitHub —and define an eight-step process involving both automated parts (e.g., using procedure based SZZ algorithm find vulnerability-contributing commits) manual analyses were fixed). The investigated classified 144 categories, take average least 4 contributing commits before being introduced, half them remain unfixed than one year. Most xmlns:xlink="http://www.w3.org/1999/xlink">contributions done developers high workload, often when doing maintenance activities, mostly addition new aiming implementing further checks inputs. conclude distilling practical implications detectors work assist timely identifying issues.

Language: Английский

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A3Test: Assertion-Augmented Automated Test case generation

Information and Software Technology, Journal Year: 2024, Volume and Issue: 176, P. 107565 - 107565

Published: Aug. 30, 2024

Language: Английский

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Automatic Test Smell Detection Using Information Retrieval Techniques

Published: Sept. 1, 2018

Software testing is a key activity to control the reliability of production code. Unfortunately, effectiveness test cases can be threatened by presence faults. Recent work showed that static indicators exploited identify test-related issues. In particular smells, i.e., sub-optimal design choices applied developers when implementing cases, have been shown related case effectiveness. While some approaches for automatic detection smells proposed so far, they generally suffer poor performance: as consequence, current detectors cannot properly provide support diagnosing quality cases. this paper, we aim at making step ahead toward automated devising novel textual-based detector, coined TASTE (Textual AnalySis Test smEll detection), with evaluating usefulness textual analysis detecting three smell types, General Fixture, Eager Test, and Lack Cohesion Methods. We evaluate in an empirical study involves manually-built dataset composed 494 instances belonging 12 software projects, comparing capabilities our detector those two code metrics-based techniques Van Rompaey et al. Greiler Our results show structural-based existing most dataset, while up 44% more effective. Finally, find structural different sets thereby indicating complementarity.

Language: Английский

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How bugs are born: a model to identify how bugs are introduced in software components

Empirical Software Engineering, Journal Year: 2020, Volume and Issue: 25(2), P. 1294 - 1340

Published: Feb. 4, 2020

Abstract When identifying the origin of software bugs, many studies assume that “a bug was introduced by lines code were modified to fix it”. However, this assumption does not always hold and at least in some cases, these are responsible for introducing bug. For example, when caused a change an external API. The lack empirical evidence makes it impossible assess how important cases therefore, which extent is valid. To advance direction, better understand bugs “are born”, we propose model defining criteria identify first snapshot evolving system exhibits This model, based on perfect test idea, decides whether observed after software. Furthermore, studied model’s carefully analyzing 116 two different open source projects. manual analysis helped classify root cause those created manually curated datasets with bug-introducing changes any code. Finally, used evaluate performance four existing SZZ-based algorithms detecting changes. We found very accurate, especially multiple commits found; F-Score varies from 0.44 0.77, while percentage true positives exceed 63%. Our results show prevalent assumption, it”, just one case system. Finding what trivial: can be developers code, or irrespective Thus, further research towards understanding projects could help improve design integration tests other procedures make development more robust.

Language: Английский

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Understanding flaky tests: the developer’s perspective

Published: Aug. 9, 2019

Flaky tests are software that exhibit a seemingly random outcome (pass or fail) when run against the same, identical code. Previous work has examined fixes to flaky and proposed automated solutions locate as well fix tests--we complement it by examining perceptions of developers about nature, relevance, challenges this phenomenon. We asked 21 professional classify 200 they previously fixed, in terms nature flakiness, origin fixing effort. analysis with information strategy. Subsequently, we conducted an online survey 121 median industrial programming experience five years. Our research shows that: The flakiness is due several different causes, four which have never been reported before, despite being most costly fix; perceived significant vast majority developers, regardless their team's size project's domain, can effects on resource allocation, scheduling, reliability test suite; report face regard mostly reproduction behavior identification cause for flakiness. Data materials [https://doi.org/10.5281/zenodo.3265785].

Language: Английский

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Generating unit tests with descriptive names or: would you name your children thing1 and thing2?

Published: July 10, 2017

The name of a unit test helps developers to understand the purpose and scenario test, names support when navigating amongst sets tests. When tests are generated automatically, however, they tend be given non-descriptive such as "test0", which provide none benefits descriptive can give test. underlying challenge is that automatically typically do not represent real scenarios have no clear other than covering code, makes naming them di cult. In this paper, we present an automated approach generates for by summarizing API-level coverage goals. optimized short, relation covered code under allow uniquely distinguish in suite. An empirical evaluation with 47 participants shows agree synthesized names, equally manually written names. Study were even more accurate faster at matching compared derived

Language: Английский

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