Developers’ perception matters: machine learning to detect developer-sensitive smells

Oliveira, Daniel; Assunção, Wesley K. G.; Garcia, Alessandro; Fonseca, Baldoíno; Ribeiro, Márcio

doi:10.1007/s10664-022-10234-2

Cited by 4 publications

(3 citation statements)

References 54 publications

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“…The automated way uses detection from measured metrics of code (complexity, coupling, lines of code, depth of inheritance tree, weighted method count, etc.) often using machine or deep learning methods: various kind of Neural Networks (Dewangan et al, 2022;Liu et al, 2021), Support Vectors Machines (Sandouka & Aljamaan, 2023;Kovačević et al, 2022;Oliveira et al, 2022;Mhawish & Gupta, 2020;Fontana et al, 2016) Random Forrest (Sandouka & Aljamaan, 2023;Kovačević et al, 2022;Fontana et al, 2016), Naive Bayes (Oliveira et al, 2022;Fontana et al, 2016), Decision Tree (Sandouka & Aljamaan, 2023;Oliveira et al, 2022;Mhawish & Gupta, 2020;Fontana et al, 2016). In this SRL, the detection or prediction ways were classified into four groups, presented in Table 5.…”

Section: Results Of Systematic Literature Reviewmentioning

confidence: 99%

“…Sandouka and Aljamaan (2023),Luburić et al (2023),Dewangan et al (2022),Oliveira et al (2022),Alkharabsheh et al (2022),Kovačević et al (2022),Liu et al (2021),Boutaib et al (2021),Draz et al (2021),Guggulothu and Moiz (2020),Mhawish and Gupta (2020), Sousa et al (2019), Singh et al (2019), Hozano et al (2018), Tufano et al (2017), Fontana et al (2016) Software quality improvement based on code smell detection Sandouka and Aljamaan (2023), Albuquerque et al (2023), Oliveira et al (2022), Draz et al (2021), Guggulothu and Moiz (2020), Mhawish and Gupta (2020), Singh et al (2019), Hozano et al (2018), Sae-Lim et al (2018), Mansoor et al (2017), Tufano et al (2017), Fontana et al (2016), Sahin et al (2014), Kessentini et al (2014) Software quality attribute -maintainability improvement based on code smell detection Luburić et al (2023), Dewangan et al (2022), Alkharabsheh et al (2022), Kovačević et al (2022), Liu et al (2021), Boutaib et al (2021), Sousa et al(2019) …”

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confidence: 99%

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Identification of Software Quality Attributes From Code Defect Prediction: A Systematic Literature Review

Rumbutis,

Slotkienė,

Pliuskuvienė

2024

New Trends in Computer Sciences

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Identifying and understanding reasons for deriving software development defects is crucial for ensuring software product quality attributes such as maintainability. This paper presents a systematic literature review and the objective is to analyze the suggestions of other authors regarding software code defect prediction using machine learning, deep learning, or other artificial intelligence methods for the identification of software quality. The systemic literature review reveals that many analyzed papers considered multiple software code defects, but they were analyzed individually. However, more is needed to identify software quality attributes. The more profound analysis of code smells indicates the significance when considering multiple detected code smells and their interconnectedness; it helps to identify the software quality sub-attributes of maintainability.

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Section: Results Of Systematic Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

Identification of Software Quality Attributes From Code Defect Prediction: A Systematic Literature Review

Rumbutis,

Slotkienė,

Pliuskuvienė

2024

New Trends in Computer Sciences

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“…A code smell is a poor code structure in a software project [25]. Developers must identify and remove code smells as soon as possible [25,249]. A well-known and widely used practice to deal with code smells is code refactoring [198].…”

Section: Enhancing Recommendations Of Composite Refactorings Based In...mentioning

confidence: 99%

On the Completeness of Composite Code Refactorings for Beneficial Smell Remova

BIBIANO¹

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2017). Ana published 12 papers (2017)(2018)(2019)(2020)(2021)(2022) in conferences with high reputation in Software Engineering. One of these papers was recognized as one of the best papers of the conference. Her Master's dissertation was the second best dissertation of Brazil in Software Engineering in 2020. She earned, in 2014, the Academic Excellence award by the Brazilian National Council for Scientific and Technological Development (CNPq) for her scientific initiation's work. Ana Carla has 13 years of experience as a software developer in Brazilian software companies. She has worked as a software developer for companies from Maceió (AL), Blumenau (SC), and Rio de Janeiro (RJ). Ana has research collaborations with international universities located in Austria, EUA, United Kingdom, and Portugal. Ana Carla is currently a research scholar in Software Engineering for the OPUS Research Group at PUC-Rio. Her main research interests are (not limited to): software refactoring, maintenance, and evolution.

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