Prioritization of god class design smell: A multi-criteria based approach

Alkharabsheh, Khalid; Alawadi, Sadi; Ignaim, Karam; Zanoon, Nabeel; Crespo, Yania; Manso, E.; Taboada, José Antonio Franco

doi:10.1016/j.jksuci.2022.09.011

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The code smells could be identified, detected, or predicted using manual/semi-automated or automated ways of artificial intelligence. Manual/semi-automated way are used for annotation code chunks based on developers' experiences (Hozano et al, 2018;Singh et al, 2019;Albuquerque et al, 2023;Luburić et al, 2023) or identification code defects based on code quality metrics such as coupling, size of methods, cohesion, or complexity (Tufano et al, 2017;Sousa et al, 2019;Alkharabsheh et al, 2022). The automated way uses detection from measured metrics of code (complexity, coupling, lines of code, depth of inheritance tree, weighted method count, etc.)…”

Section: Results Of Systematic Literature Reviewmentioning

confidence: 99%

“…Scientific papers published in 2019 highlight software quality attributes, such as maintainability, difficulty maintaining, and issues with repairing the detected bug. The authors (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) noticed that left code defects strongly impacted software maintainability (see Table 6). The answer to the main scientific question (MRQ) is more complicated because to detect or predict code smell separately and does not consider possible connections between them, the impacts on software quality identification should be more accurate.…”

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%

Section: Results Of Systematic Literature Reviewmentioning

confidence: 99%

mentioning

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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Prioritizing God Class Code Smells in Object-Oriented Software Using Fuzzy Inference System

Verma,

Kumar,

Verma

2024

Arab J Sci Eng

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FedCSD: A Federated Learning Based Approach for Code-Smell Detection

Alawadi,

Alkharabsheh,

Alkhabbas

et al. 2024

IEEE Access

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Software quality is critical, as low quality, or "Code smell," increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new patterns and practices in code quality management. However, the current literature is still missing such capabilities. This paper addresses the previous challenges by proposing a Federated Learning Code Smell Detection (FedCSD) approach, specifically targeting "God Class," to enable organizations to train distributed ML models while safeguarding data privacy collaboratively. We conduct experiments using manually validated datasets to detect and analyze code smell scenarios to validate our approach. Experiment 1, a centralized training experiment, revealed varying accuracies across datasets, with dataset two achieving the lowest accuracy (92.30%) and datasets one and three achieving the highest (98.90% and 99.5%, respectively). Experiment 2, focusing on cross-evaluation, showed a significant drop in accuracy (lowest: 63.80%) when fewer smells were present in the training dataset, reflecting technical debt. Experiment 3 involved splitting the dataset across 10 companies, resulting in a global model accuracy of 98.34%, comparable to the centralized model's highest accuracy. The application of federated ML techniques demonstrates promising performance improvements in code-smell detection, benefiting both software developers and researchers.

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Prioritization of god class design smell: A multi-criteria based approach

Cited by 3 publications

References 15 publications

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

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

Prioritizing God Class Code Smells in Object-Oriented Software Using Fuzzy Inference System

FedCSD: A Federated Learning Based Approach for Code-Smell Detection

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