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

Barriga, Angela; Bettini, Lorenzo; Iovino, Ludovico; Rutle, Adrian; Heldal, Rogardt

doi:10.5381/jot.2021.20.3.a1

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…As for the eight foundations papers, Barriga et al (Barriga et al 2021) build on top of PARMOREL, a reinforcement learningbased framework for automatically refactoring models. They extend PARMOREL to support smells detection and selective refactoring based on quality characteristics.…”

Section: Accepted Papersmentioning

confidence: 99%

Preface to the JOT special issue containing the ECMFA 2021 selected papers.

Burgueño¹,

Gogolla²

2021

JOT

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In this preface, the editors present an overview of the topics and scope of the European Conference on Modelling Foundations and Applications (ECMFA), and describe the editorial and reviewing process for its 17th edition (ECMFA 2021).The papers selected for publication and presentation are presented and briefly described, as well as the details about the keynote talk by Prof. Jean-Marc Jézéquel. Finally, the ECMFA committees are acknowledged.

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Section: Accepted Papersmentioning

confidence: 99%

Preface to the JOT special issue containing the ECMFA 2021 selected papers.

Burgueño¹,

Gogolla²

2021

JOT

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“…For handling such a spiral of complexity, solutions that minimize software complexity through incrementally increasing internal software quality are urgently needed. Refactoring is the study domain which that tackles such topic [2,3].…”

Section: Introductionmentioning

confidence: 99%

Software Code Refactoring: A Comprehensive Review

Al-shakarjy¹,

Taha²

2023

JES

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The complexity of software has increased because of the development as well as the difficulty of requirements during the development of software, or to add new features that eventually lead to reduce the quality of the software as a whole. Software refactoring can be defined as included processes in the maintenance period of a software life cycle, and it is a technique to clean the software code from code bad smell and to improve the internal structure of the software, in addition to increasing the quality of software by using a set of activities without changing the external behavior of a software. Researchers have been developing techniques to reform software during the code or design standard to decrease the effort and time required for maintenance processes. This paper provides a systematic review of the literature for 17 studies of code standards, An automatic search is utilized in the digital libraries to look for the relevant studies that were published from 2014 to 2021, the best five researchers are chosen in this subject, five studies or less are chosen for each depending on the number of reference in the database of scientific sites, or using an approach or a new method to get good results. Eventually, each paragraph is analyzed and mentions the method or algorithm used in rebuilding software, further the aims, and the result for each paper.

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

2022

Self Cite

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

Cited by 5 publications

References 28 publications

Preface to the JOT special issue containing the ECMFA 2021 selected papers.

Preface to the JOT special issue containing the ECMFA 2021 selected papers.

Software Code Refactoring: A Comprehensive Review

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

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