On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation

Palomba, Fabio; Bavota, Gabriele; Penta, Massimiliano Di; Fasano, Fausto; Oliveto, Rocco; Lucia, Andrea De

doi:10.1007/s10664-017-9535-z

Cited by 241 publications

(218 citation statements)

References 46 publications

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“…Among all these studies, Khomh et al [52] showed that the presence of sub-optimal implementations in Java classes, i.e., code smells, has a strong impact on the likelihood that such classes will be often modified by developers. The results were later confirmed by several studies in the field [22,75,83,107], further highlighting the relevance of code smells for change-proneness. Our work is clearly based on these findings, and aims at providing additional evidence of how code smells can be adopted in the context of prediction models having the goal of identifying change-prone classes.…”

Section: Related Worksupporting

confidence: 64%

“…Change-prone classes represent source code components that, for different reasons, tend to change more often than others. This phenomenon has been widely investigated by the research community [52,68,26,14,105,83] with the aim of studying the factors contributing to the change-proneness of classes. Among all these studies, Khomh et al [52] showed that the presence of sub-optimal implementations in Java classes, i.e., code smells, has a strong impact on the likelihood that such classes will be often modified by developers.…”

Section: Related Workmentioning

confidence: 99%

“…Fowler defined "bad code smells" (shortly, "code smells" or simply "smells") as "symptoms of the presence of poor design or implementation choices applied during the development of a software system" [35]. Starting from there, several researchers heavily investigated (i) how code smells evolve over time [84,86,91,119,120,121], (ii) the way developers perceive them [79,111,126], and (iii) what is their impact on non-functional attributes of source code [1,36,50,52,77,83,104,125]. All these studies came up with a shared conclusion: code smells negatively impact program comprehension, maintainability of source code, and development costs.…”

Section: Code Smell Detection and Prioritizationmentioning

confidence: 99%

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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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Section: Related Worksupporting

confidence: 64%

Section: Related Workmentioning

confidence: 99%

Section: Code Smell Detection and Prioritizationmentioning

confidence: 99%

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Improving change prediction models with code smell-related information

et al. 2019

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“…Possible future work could also be related to the investigation of the harmfulness of the different SonarQube issues, including their fault-and change-proneness. This area has already been widely investigated considering code smells [14], [17], [38], [31] but researchers have never considered TD issues detected by SonarQube from the point of view of fault-and change-proneness.…”

Section: Significancementioning

confidence: 99%

The Technical Debt Dataset

Lenarduzzi

Saarimäki

Taibi

2019

Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering

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Technical Debt analysis is increasing in popularity as nowadays researchers and industry are adopting various tools for static code analysis to evaluate the quality of their code. Despite this, empirical studies on software projects are expensive because of the time needed to analyze the projects. In addition, the results are difficult to compare as studies commonly consider different projects. In this work, we propose the Technical Debt Dataset, a curated set of project measurement data from 33 Java projects from the Apache Software Foundation. In the Technical Debt Dataset, we analyzed all commits from separately defined time frames with SonarQube to collect Technical Debt information and with Ptidej to detect code smells. Moreover, we extracted all available commit information from the git logs, the refactoring applied with Refactoring Miner, and fault information reported in the issue trackers (Jira). Using this information, we executed the SZZ algorithm to identify the fault-inducing and -fixing commits. We analyzed 78K commits from the selected 33 projects, detecting 1.8M SonarQube issues, 38K code smells, 28K faults and 57K refactorings. The project analysis took more than 200 days. In this paper, we describe the data retrieval pipeline together with the tools used for the analysis. The dataset is made available through CSV files and an SQLite database to facilitate queries on the data. The Technical Debt Dataset aims to open up diverse opportunities for Technical Debt research, enabling researchers to compare results on common projects.

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“…In the immediate future, we plan to elaborate on Type 1 ReD as there is no empirical evidence of harmfulness of requirement smells, according to the definition and the detection approach proposed by Femmer et al [18]. We will follow the approaches widely adopted to assess the harmfulness of code smells on different software qualities [21], [22], [23], [24]. We will triangulate data from requirements platforms, such as issue trackers and requirements repositories platforms [25], with studies involving requirements engineers, business analysts, and software developers [26].…”

Section: B Requirement Smells Harmfulnessmentioning

confidence: 99%

Towards a Holistic Definition of Requirements Debt

Lenarduzzi

Fucci

2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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When not appropriately managed, technical debt is considered to have negative effects to the long term success of software projects. However, how the debt metaphor applies to requirements engineering in general, and to requirements engineering activities in particular, is not well understood. Grounded in the existing literature, we present a holistic definition of requirements debt which includes debt incurred during the identification, formalization, and implementation of requirements. We outline future assessment to validate and further refine our proposed definition. This conceptualization is a first step towards a requirements debt monitoring framework to support stakeholders decisions, such as when to incur and eventually pay back requirements debt, and at what costs.

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On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation

Cited by 241 publications

References 46 publications

Improving change prediction models with code smell-related information

Improving change prediction models with code smell-related information

The Technical Debt Dataset

Towards a Holistic Definition of Requirements Debt

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