An Automatic Advisor for Refactoring Software Clones Based on Machine Learning

Sheneamer, Abdullah

doi:10.1109/access.2020.3006178

Cited by 19 publications

(8 citation statements)

References 27 publications

(43 reference statements)

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“…Furthermore, by achieving issues prediction, modelers could be aware of which models are more prone to get corrupted, and with enough advance, the repair process could be even prevented. Regarding code refactoring, different ML techniques [76,77] have been applied to predict and identify which parts of the code are prone to be refactored. By doing so, the time spent on refactoring is reduced.…”

Section: Predictionmentioning

confidence: 99%

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

2022

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Artificial intelligence has already proven to be a powerful tool to automate and improve how we deal with software development processes. The application of artificial intelligence to model-driven engineering projects is becoming more and more popular; however, within the model repair field, the use of this technique remains mostly an open challenge. In this paper, we explore some existing approaches in the field of AI-powered model repair. From the existing approaches in this field, we identify a series of challenges which the community needs to overcome. In addition, we present a number of research opportunities by taking inspiration from other fields which have successfully used artificial intelligence, such as code repair. Moreover, we discuss the connection between the existing approaches and the opportunities with the identified challenges. Finally, we present the outcomes of our experience of applying artificial intelligence to model repair.

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Section: Predictionmentioning

confidence: 99%

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

2022

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“…Sheneamer [ 27 ] proposed an approach to automatically suggest a treatment about refactoring of clones in the system. It suggests whether a clone class needs refactoring and what type of refactoring is needed by a clone.…”

Section: Literature Surveymentioning

confidence: 99%

Historical perspective of code clone refactorings in evolving software

et al. 2022

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Cloning in software is generally perceived as a threat to its maintenance and that is why it needs to be managed properly. Understanding clones from a historical perspective is essential for effective clone management. Analysis of code refactorings performed on clones in previous releases will help developers in taking decisions about clone refactoring in future releases. In this paper we perform a longitudinal study on the evolution of clone refactorings in various versions of five software systems. To perform a systematic study on clone refactoring evolution, we define clone evolution patterns for studying refactorings in a formal notation. Our results show that only a small proportion of code clones are refactored between the versions and most of the refactorings are inconsistent within clone classes. Moreover, clone refactorings may cause clone removal. Analysis of the source code of refactored clones reveals similar reasons of inconsistent refactorings and clone removal for five Java systems. This analysis will help in devising appropriate strategies for managing clone refactorings in software and hence provide foundation for devising better clone management tools.

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“…The scope of the smells detected using the Edelta DSL is automatically detectable smells, but users just need to define their own smells at code level without needing to train on any dataset. Regarding code refactoring, different ML techniques (Alenezi et al 2020;Sheneamer 2020) have been applied to predict and identify which parts of the code are prone to be refactored. By doing so, the time spent in refactoring can be reduced.…”

Section: Approaches For Mdementioning

confidence: 99%

Addressing the trade off between smells and quality when refactoring class diagrams.

Barriga¹,

Bettini²,

Iovino³

et al. 2021

JOT

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Models are core artifacts of modern software engineering processes, and they are subject to evolution throughout their life cycle due to maintenance and to comply with new requirements as any other software artifact. Smells in modeling are indicators that something may be wrong within the model design. Removing the smells using refactoring usually has a positive effect on the general quality of the model. However, it could have a negative impact in some cases since it could destroy the quality wanted by stakeholders. PARMOREL is a framework that, using reinforcement learning, can automatically refactor models to comply with user preferences. The work presented in this paper extends PARMOREL to support smells detection and selective refactoring based on quality characteristics to assure only the refactoring with a positive impact is applied. We evaluated the approach on a large available public dataset to show that PARMOREL can decide which smells should be refactored to maintain and, even improve, the quality characteristics selected by the user.

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An Automatic Advisor for Refactoring Software Clones Based on Machine Learning

Cited by 19 publications

References 27 publications

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

Historical perspective of code clone refactorings in evolving software

Addressing the trade off between smells and quality when refactoring class diagrams.

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