Artificial Intelligence for Software Engineering: The Journey So Far and the Road Ahead

Artificial intelligence and recent advances in deep learning architectures, including transformer networks and large language models, change the way people think and act to solve problems. Software engineering, as an increasingly complex process to design, develop, test, deploy, and maintain large-scale software systems for solving real-world challenges, is profoundly affected by many revolutionary artificial intelligence tools in general and machine learning in particular. In this roadmap for artificial intelligence in software engineering, we highlight the recent deep impact of artificial intelligence on software engineering by discussing successful stories of applications of artificial intelligence to classic and new software development challenges. We identify the new challenges that the software engineering community has to address in the coming years to successfully apply artificial intelligence in software engineering, and we share our research roadmap toward the effective use of artificial intelligence in the software engineering profession, while still protecting fundamental human values. We spotlight three main areas that challenge the research in software engineering: the use of generative artificial intelligence and large language models for engineering large software systems, the need of large and unbiased datasets and benchmarks for training and evaluating deep learning and large language models for software engineering, and the need of a new code of digital ethics to apply artificial intelligence in software engineering.

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Publication Details

Journal
ACM Transactions on Software Engineering and Methodology
Published
2025-04-18
DOI
https://doi.org/10.1145/3719006
Citations
10
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
24.81

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article

Artificial Intelligence for Software Engineering: The Journey So Far and the Road Ahead

Alexander Chatzigeorgiou, Xin Xia, Aldeida Aleti, Denys Poshyvanyk et al.
10 citations
ACM Transactions on Software Engineering and Methodology
Explainable Artificial Intelligence (XAI)
24.81
article

Artificial Intelligence for Software Engineering: The Journey So Far and the Road Ahead

Alexander Chatzigeorgiou, Xin Xia, Aldeida Aleti, Denys Poshyvanyk, Xing Hu, Mauro Pezzè, Haipeng Cai, Iftekhar Ahmed, Pinjia He
article en
10 citations

Abstract

Artificial intelligence and recent advances in deep learning architectures, including transformer networks and large language models, change the way people think and act to solve problems. Software engineering, as an increasingly complex process to design, develop, test, deploy, and maintain large-scale software systems for solving real-world challenges, is profoundly affected by many revolutionary artificial intelligence tools in general and machine learning in particular. In this roadmap for artificial intelligence in software engineering, we highlight the recent deep impact of artificial intelligence on software engineering by discussing successful stories of applications of artificial intelligence to classic and new software development challenges. We identify the new challenges that the software engineering community has to address in the coming years to successfully apply artificial intelligence in software engineering, and we share our research roadmap toward the effective use of artificial intelligence in the software engineering profession, while still protecting fundamental human values. We spotlight three main areas that challenge the research in software engineering: the use of generative artificial intelligence and large language models for engineering large software systems, the need of large and unbiased datasets and benchmarks for training and evaluating deep learning and large language models for software engineering, and the need of a new code of digital ethics to apply artificial intelligence in software engineering.

ACM Transactions on Software Engineering and MethodologyVol. 34(5)
William & Mary (US), Chinese University of Hong Kong (HK), University of Macedonia (GR), University of California, Irvine (US), Huawei Technologies (China) (CN), Williams (United States) (US), Monash University (AU), Università della Svizzera italiana (CH), University at Buffalo, State University of New York (US), Zhejiang University (CN)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 1%
Explainable Artificial Intelligence (XAI)
24.81
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