Systematic Mapping: Artificial Intelligence Techniques in Software Engineering

Sofian, Hazrina; Yunus, Nur Arzilawati Md; Ahmad, Rodina

doi:10.1109/access.2022.3174115

Cited by 12 publications

(10 citation statements)

References 76 publications

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“…Artificial Intelligence (AI) is increasingly being adopted in software engineering (SE), enhancing productivity, software quality, and decision-making [1,2]. AI's role in SE spans from code completion to defect detection [1].…”

Section: Ai and Software Engineeringmentioning

confidence: 99%

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Gunning Fog Index and Automated UML Generation

Djaber,

HADJADJ

2023

Preprint

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This research investigates the influence of the Gunning Fog Index (GFI) on the production of PlantUML use case diagrams by ChatGPT, within the context of software development projects, particularly focusing on E-commerce for Special Shoes. The study emphasizes the impact of linguistic complexity, as measured by GFI, on the automated code-generation process. It reveals that lower GFI scores, indicative of clearer text, lead to more easily comprehensible code representations, while higher scores correlate with intricate but potentially less accessible code artifacts. This reflects a nuanced balance between detail and clarity in the specifications. A key finding is the significant contribution of integrating linguistic clarity met-rics like GFI into the code generation workflow. This integration is shown to enhance code quality, usability, and facilitate collaboration among stakeholders. Despite the study’s focus on GFI to the exclusion of other readability metrics, it provides valuable insights into the role of linguistic complexity in shaping software artifacts generated by ChatGPT. Recognizing the importance of readability allows developers to more effectively translate natural language specifications into machine-generated code, yielding benefits in diverse project scenarios.

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Section: Ai and Software Engineeringmentioning

confidence: 99%

“…AI's role in SE spans from code completion to defect detection [1]. However, existing studies often focus on specific AI techniques or SE phases, with challenges in AI's integration into SE still prevalent [2,3]. The emergence of Explainable AI (XAI) in SE highlights the need for transparency in AI models [1].…”

Section: Ai and Software Engineeringmentioning

confidence: 99%

Gunning Fog Index and Automated UML Generation

Djaber,

HADJADJ

2023

Preprint

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“…Software companies shifted their focus to deploying AI paradigms to their existing development processes. The academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems [5] and critical activities [6]. Examples include software testing [7], maintenance [8], requirements extraction [9], ambiguity resolution [10], software vulnerability detection [11], and software engineering education [12].…”

Section: Introductionmentioning

confidence: 99%

“…Examples include software testing [7], maintenance [8], requirements extraction [9], ambiguity resolution [10], software vulnerability detection [11], and software engineering education [12]. Despite the increasing prevalence of AI use in software engineering, a comprehensive and holistic understanding of the current status, possible target applications, practical software engineering usage scenarios, and unavoidable limitations, ethical concerns, and challenges remain unclear [6].…”

Section: Introductionmentioning

confidence: 99%

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

Kokol

2024

Preprint

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Artificial intelligence (AI) has witnessed an exponential increase in its use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems. However, a comprehensive and holistic understanding of the current status is missing. To close the above gap, synthetic knowledge synthesis was used to induce a research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and five themes, namely natural language processing in software engineering, use of artificial intelligence in the management of software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n=2042), followed by the United States (n=1193), India (n=934), Germany (n=445), and Canada (n=381). A high percentage (n=47.4%) of papers were funded, showing a strong interest in this research topic. The convergence of AI and software engineering can significantly reduce needed resources, improve quality, increase user experience, and improve the well-being of software developers.

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“…As a result of this complexity, the usage of AI techniques for bug prediction has grown, signaling a substantial shift in how bug identification is conducted. (Wangoo, 2018;Tantithamthavorn and Jiarpakdee, 2021;Bommi and Negi, 2023;Sofian et al, 2022). Exploration of machine learning models to predict bugs in source code takes advantage of AI's ability to analyze large and complex software datasets (Shan et al, 2014;Habibi et al, 2018;Soe et al, 2018;Rahim et al, 2021;Aleithan, 2021;Shen et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Bug Prediction Models: seeking the most efficient

Marçal,

Garcia

2024

Preprint

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Choosing the most appropriate machine learning model for bug prediction tasks is critical. This paper primarily compares the predictive power of individual models versus ensemble models. We begin by experimenting with popular single-machine learning models commonly used in bug prediction, like neural networks and support vector machines. Additionally, we test with ensemble models that combine individual models' unique strengths, aiming to maximize each's benefits. Our evaluation is based on datasets containing historical development data from well-known open-source software projects. We rely on various metrics when assessing the models, encompassing accuracy, precision, recall, and F1 score. Based on our research findings, it has been observed that ensemble models tend to outperform single models, particularly when it comes to maintaining resilience across various datasets.Nevertheless, factors like the project's complexity, data availability, and computational resources all play a role in determining whether to use single or ensemble models. This paper offers a thorough analysis of the factors to consider when selecting machine learning models and approaches for bug prediction, providing valuable insights into the field. Furthermore, it offers practical advice for professionals, enabling them to make informed choices.

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Systematic Mapping: Artificial Intelligence Techniques in Software Engineering

Cited by 12 publications

References 76 publications

Gunning Fog Index and Automated UML Generation

Gunning Fog Index and Automated UML Generation

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

Bug Prediction Models: seeking the most efficient

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