Software smell detection techniques: A systematic literature review

AbuHassan, Amjad; Alshayeb, Mohammad; Ghouti, Lahouari

doi:10.1002/smr.2320

Cited by 29 publications

(13 citation statements)

References 170 publications

(327 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We recently developed a systematic procedure for manual code smell annotation to alleviate this issue and used it to label a moderate-sized code smell dataset for the C# language [13] for two code smells: Large Class and Long Method. We chose the C# language, as it dominates the developers' discussions on code smells [14] but is neglected in terms of available code analysis tools [14], [8], [9]. In this paper, we use our C# dataset to train and evaluate an ML-based code smell detector.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of code smells using metrics and CodeT5 embeddings: a case study in C#

Kovačević¹,

Luburić²,

Slivka³

et al. 2022

Preprint

View full text Add to dashboard Cite

Code smells are code structures that harm the software’s quality. An obstacle to developing automatic detectors is the available datasets' limitations. Furthermore, researchers developed many solutions for Java while neglecting other programming languages. Recently, we created the code smell dataset for C# by following an annotation procedure inspired by the established annotation practices in Natural Language Processing. This paper evaluates Machine Learning (ML) code smell detection approaches on our novel dataset. We consider two feature representations to train ML models: (1) code metrics and (2) CodeT5 embeddings. This study is the first to consider the CodeT5 state-of-the-art neural source code embedding for code smell detection in C#. To prove the effectiveness of ML, we consider multiple metrics-based heuristics as alternatives. In our experiments, the best-performing approach was the ML classifier trained on code metrics (F-measure of 0.87 for Long Method and 0.91 for Large Class detection). However, the performance improvement over CodeT5 features is negligible if we consider the advantages of automatically inferring features. We showed that our model exceeds human performance and could be helpful to developers. To the best of our knowledge, this is the first study to compare the performance of automatic smell detectors against human performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic detection of code smells using metrics and CodeT5 embeddings: a case study in C#

Kovačević¹,

Luburić²,

Slivka³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Software refactoring, deemed as a viable process to enhance software quality, can be applied at the code and design levels. 15 However, this process is not foolproof as applying all refactoring opportunities does not necessarily lead to optimal software quality. 16 Moreover, applying refactoring in different order produces different software quality.…”

Section: Related Workmentioning

confidence: 99%

Software refactoring side effects

AbuHassan

Alshayeb

Ghouti

2021

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

Software refactoring solutions aim at mitigating the negative effects of code and design smells on the overall software quality. Many efforts have been exerted to improve the software refactoring process. However, most of these efforts, despite their contributions, overlooked the side effects of the identified refactoring opportunities that may lead to new smells that will go unnoticed. This paper addresses the side effects of software refactoring and proposes sound solutions for handling them. Unlike current practices in software maintenance, we recommend three different approaches to handle the refactoring side effects. In the first approach, called the baseline, we opt to ignore the smells, caused by refactoring, while executing the identified refactoring decisions. In the second one, refactoring decisions are continually updated to fix all smells caused by side effects. In the last approach, only a subset of these smells is appended to the original smell sequence during the execution of the refactoring decisions. Thanks to the proposed approaches, optimal refactoring decisions are identified using a multi‐objective (MO) optimization algorithm commonly known as the MO covariance matrix adaptation evolution strategy (MO‐CMA‐ES). Experiment results corroborate our assumptions and show the superiority of the second approach over the other ones.

show abstract

“…Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java. C# language dominates the developers' discussions on code smells' (Tahir et al, 2020) but is poorly supported in code analysis tools (Tahir et al, 2020;AbuHassan et al, 2021). Finally, C# is similar to the Java programming language, making our contributions relevant to the large research community.…”

Section: Introductionmentioning

confidence: 99%

Towards a systematic approach to manual annotation of code smells

Luburić¹,

Prokić²,

Grujić³

et al. 2022

Preprint

View full text Add to dashboard Cite

<div>Code smells are structures in code that indicate the presence of maintainability issues. A significant problem with code smells is their ambiguity. They are challenging to define, and software engineers have a different understanding of what a code smell is and which code suffers from code smells.</div><div>A solution to this problem could be an AI digital assistant that understands code smells and can detect (and perhaps resolve) them. However, it is challenging to develop such an assistant as there are few usable datasets of code smells on which to train and evaluate it. Furthermore, the existing datasets suffer from issues that mostly arise from an unsystematic approach used for their construction.</div><div>Through this work, we address this issue by developing a procedure for the systematic manual annotation of code smells. We use this procedure to build a dataset of code smells. During this process, we refine the procedure and identify recommendations and pitfalls for its use. The primary contribution is the proposed annotation model and procedure and the annotators’ experience report. The dataset and supporting tool are secondary contributions of our study. Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java.</div>

show abstract

Software smell detection techniques: A systematic literature review

Cited by 29 publications

References 170 publications

Automatic detection of code smells using metrics and CodeT5 embeddings: a case study in C#

Automatic detection of code smells using metrics and CodeT5 embeddings: a case study in C#

Software refactoring side effects

Towards a systematic approach to manual annotation of code smells

Contact Info

Product

Resources

About