Artificial Intelligence and Machine Learning in Software Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 44633
Special Issue Editor
Interests: mobile computing; search-based software engineering; genetic programming; context-aware systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The next decade will be full of technological advances that will present numerous challenges and opportunities for the software engineering (SE) discipline. The application of artificial intelligence and machine learning (AI/ML) techniques to SE has been extensively studied, but many issues and areas of application remain open for investigation. The growing ability of machines to learn and act intelligently will continue to guide the transformation process of the world in which we live in. Forbes identifies AI/ML as the leading strategic technology trend for the next decade, and the IDC indicates that, even with the effects of the pandemic being felt globally, growth in this area is expected to continue accelerating during the next quinquennium. There is an urgent need to explore the opportunities created by AI/ML to leverage research in the area of SE, as software and application vendors should continue incorporating AI/ML as a differentiating aspect to increase adoption and deliver benefits to their customers and users, as well as improve return on investment and achieve cost savings.
Search-based software engineering (SBSE) seeks to reformulate SE challenges as search-based optimization problems, typically through the application of evolutionary algorithms or ML. It has already been applied to a wide variety of areas in SE, including requirements engineering, project planning and cost estimation, automated maintenance, or quality assessment. Most of the global literature in the area of SBSE is, however, dedicated to applications related to software testing – with the generation of test data being the most studied topic. Leveraging AI for automating the processes of creating, executing, and verifying software testing remains critical for improving the quality of the complex software systems that have become the norm of modern society.
The application of ML techniques also possesses enormous potential for applicability in the area of process mining and for improving the quality of processes in an enterprise environment, particularly in the context of DevOps practices. The intricate software development ecosystems of modern enterprises, with emphasis on continuous integration/continuous delivery (CI/CD) pipelines, include several sources that can help understand the quality of processes and products—such as source code repositories and version control systems, static analysis tools, continuous integration servers, testing tools, bug tracking systems, or incident management tools. However, to achieve this goal, it is necessary to systematically collect, store, and analyze information; raw data can then, through ML and data mining automated mechanisms, generate valuable information capable of providing insight, supporting strategies for enabling the identification and even the prediction of defects and failures, and allowing teams to prioritize efforts through alert and recommendation mechanisms.
Prof. José Carlos Bregieiro Ribeiro
Guest Editor
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Keywords
- Software engineering
- artificial intelligence and machine learning
- search-based software engineering
- search-based test data generation
- process mining
- data mining
- evolutionary testing
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