A machine learning approach to software model refactoring

Sidhu, Brahmaleen Kaur; Singh, Kawaljeet; Sharma, Neeraj

doi:10.1080/1206212x.2020.1711616

Cited by 25 publications

(13 citation statements)

References 14 publications

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“…Tackling model refactoring, in [47] the authors make use of a deep NN architecture to refactor UML diagrams with symptoms of design flaws. In this approach, the deep NN learns to recognize the presence of functional decomposition in UML models of object-oriented software, producing as output a refactored model without flaws.…”

Section: Neural Networkmentioning

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: Neural Networkmentioning

confidence: 99%

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

2022

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“…However, there is no automated detection of refactoring opportunities since it is based on stakeholders refactoring goals and recommendations. In [33], the authors use the machine learning approach to refactor a UML class diagram suffering from a functional decomposition problem. This solution is not generic; this work focuses on one specific problem and tries to transform the UML class diagram into a new version more compliant with the Object Oriented paradigm.…”

Section: Related Workmentioning

confidence: 99%

Decision tree-based Design Defects Detection

et al. 2021

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Design defects affect project quality and hinder development and maintenance. Consequently, experts need to minimize these defects in software systems. A promising approach is to apply the concepts of refactoring at higher level of abstraction based on UML diagrams instead of code level. Unfortunately, we find in literature many defects that are described textually and there is no consensus on how to decide if a particular design violates model quality. Defects could be quantified as metrics based rules that represent a combination of software metrics. However, it is difficult to find manually the best threshold values for these metrics. In this paper, we propose a new approach to identify design defects at the model level using the ID3 decision tree algorithm. We aim to create a decision tree for each defect. We experimented our approach on four design defects: The Blob, Data class, Lazy class and Feature Envy defect, using 15 Object-Oriented metrics. The rules generated using decision tree give a very promising detection results for the four open source projects tested in this paper. In Lucene 1.4 project, we found that the precision is 67% for a recall of 100%. In general, the accuracy varies from 49%, reaching for Lucene 1.4 project 80%.INDEX TERMS Anti-patterns, Bad smells, Decision tree, Model refactoring, Object oriented metrics.

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“…In (Burgueño et al 2019) authors present a NN architecture for model transformation without specifying code for any specific transformations. Tackling model refactoring, in (Sidhu et al 2020) authors make use of a deep NN architecture to refactor UML diagrams with symptoms of design flaws. NN need a great amount of data in order to work.…”

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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A machine learning approach to software model refactoring

Cited by 25 publications

References 14 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

Decision tree-based Design Defects Detection

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

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