Call for papers: Applications of Computational Intelligence Techniques to Software Engineering Problems
Loveleen Gaur, Amity University, India
Gurinder Singh, Group VC, Amity Universities, India
Mike Hinchey, University of Limerick, UK
Gurmeet Singh, University of South Pacific, Fiji
Vishal Jain, Bharati Vidyapeeth's Institute of Computer Applications and Management, India
As software systems become larger and more complex, they bring various challenges. Before deployment, software undergoes various stages of development such as requirements elicitation, software designing, software project planning, software coding, software testing and maintenance. Every stage is bundled with several costly and error-prone tasks or activities. Thus, we need to explore computational intelligence techniques to carry out different software engineering tasks.
Computational intelligence is closely related to artificial intelligence where heuristic as well as metaheuristic algorithms are designed to provide better and optimized solution in a reasonable amount of time. These algorithms have been successfully applied to different application domains such as medical, bioinformatics, computer networks (for routing and scheduling), forecasting, etc. In addition, researchers have applied intelligent techniques to various domains of software engineering such as software requirement prioritization, software cost estimation, reliability assessment, software defect prediction, maintainability prediction, quality prediction, size estimation, software vulnerability prediction, software test case prioritization and many more.
Computational techniques such as evolutionary algorithms, machine learning approaches, meta-heuristic algorithms, etc., all constitute different types of intelligent behaviour. Optimization algorithms can be utilized for obtaining a solution to a problem where the goals or targets to be achieved are known. Machine learning algorithms are used when we have enough data using which knowledge can be extracted and models can be trained. For example, models can be developed for predicting error prone classes of software. A meta-heuristic is a high-level, iterative process that guides and manipulates an underlying heuristic to efficiently explore the search space. The underlying heuristic can be a local search, or a low or high-level procedure. Meta-heuristics provide near optimal solutions with high accuracy and limited resources in a reasonable amount of time by exploiting the search space.
For this special issue, we invite researchers, academicians and professionals to contribute research papers expressing their ideas and research in the application of intelligent techniques to the field of software engineering. We are interested in full length, original and unpublished papers, based on theoretical and experimental contributions.
Relevant topics include but are not limited to:
- Computational intelligence techniques for improving software engineering
- Software quality prediction using intelligent techniques
- Intelligent feature selection techniques
- Computational techniques to solve class imbalance problem
- Metaheuristics for test case Optimization
- Test case generation using intelligent algorithms
- Intelligent requirement elicitation to optimize software quality
- Software cost estimation models using machine learning techniques
- Artificial intelligence in predictive maintenance
- Developing intelligent systems for software design
- Use of intelligent techniques for analysing software repositories
- Software Reliability prediction using intelligent techniques
Full paper submission: 15 September 2020
First review notification: 15 January 2021
Revised paper submission: 10 February 2021
Second review notification (if required): 5 March 2021
Final decision notification: 20 April 2021
- Submit paper as article type 'Original research'
- Later in submission process, confirm that your paper belongs to a special issue
- Select title of special issue from menu 'S.I. : ACITSEP’
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