Special Issues

Emerging Artificial Intelligence Techniques for Software Engineering Process Optimization

Submission Deadline: 15 March 2024 (closed) View: 79

Guest Editors

Dr. Muhammad Azeem Akbar, Lappeenranta University of Technology, Finland.
Dr. Sajjad Mahmood, King Fahd University of Petroleum and Minerals, Saudi Arabia.

Summary

In every sphere of technology nowadays, the world has been moving away from manual procedures towards more intelligent systems that minimize human error and intervention, and software engineering is no exception. As software engineering discipline is the result of advancement in the field of technology.


There has been a recent surge in interest in the application of Artificial Intelligence (AI) techniques to Software Engineering (SE) problems. This concept is typified by recent advancements in the software engineering domain, but also by long-established work in probabilistic reasoning and machine learning for SE. Talking AI, it is a comparatively fresh field in software engineering ready to acknowledge challenges. On the other hand, SE is the commanding industrial field. Along these lines, automating SE (automated design, testing, effort estimation, etc.) is the most applicable test today.


Besides software engineering phases, AI also gives compliments in better software project management and decision-making. Making better decisions is not only a necessary aspect of management but for teams who deliver software as well since every decision has a flow-on effect. How we make decisions influences an organization’s agility, culture, and ability to successfully deliver software that delights its customers; and AI has the ability to engage SE in task prioritizations and to fix the multicriteria decision-making problems.


The objective of this special issue is to elucidate the various techniques of intelligent computing that have been applied to software engineering stages and management processes, as well as the scope for some of these techniques to solve existing challenges and optimize software development processes.


Despite the focus of this Special Issue is AI and software engineering, as well as multicriteria decision-making techniques, fuzzy analysis, and statistical approaches, we welcome contributions in all areas of intelligent software engineering, as well as in the topics detailed below. We strongly encourage interdisciplinary work in these areas.


Keywords

Software engineering automation
Software engineering optimization
Software Design Automation
Software testing automation
Smart software project management techniques
AI-enabled Microservices
Global software process control
Decision-making techniques
Data science for process optimization
Software security estimation
DevOps pipeline automation
Effort estimation techniques
Risk mitigation tools
Statistical analysis/modeling and its diagnostics

Published Papers


  • Open Access

    ARTICLE

    Software Cost Estimation Using Social Group Optimization

    Sagiraju Srinadhraju, Samaresh Mishra, Suresh Chandra Satapathy
    Computer Systems Science and Engineering, DOI:10.32604/csse.2024.055612
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Techniques for Software Engineering Process Optimization)
    Abstract This paper introduces the integration of the Social Group Optimization (SGO) algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model (COCOMO). COCOMO’s fixed coefficients often limit its adaptability, as they don’t account for variations across organizations. By fine-tuning these parameters with SGO, we aim to improve estimation accuracy. We train and validate our SGO-enhanced model using historical project data, evaluating its performance with metrics like the mean magnitude of relative error (MMRE) and Manhattan distance (MD). Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost More >

  • Open Access

    ARTICLE

    Test Case Generation Evaluator for the Implementation of Test Case Generation Algorithms Based on Learning to Rank

    Zhonghao Guo, Xinyue Xu, Xiangxian Chen
    Computer Systems Science and Engineering, Vol.48, No.2, pp. 479-509, 2024, DOI:10.32604/csse.2023.043932
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Techniques for Software Engineering Process Optimization)
    Abstract In software testing, the quality of test cases is crucial, but manual generation is time-consuming. Various automatic test case generation methods exist, requiring careful selection based on program features. Current evaluation methods compare a limited set of metrics, which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment. To address this, we propose an evaluation tool, the Test Case Generation Evaluator (TCGE), based on the learning to rank (L2R) algorithm. Unlike previous approaches, our method comprehensively evaluates algorithms by considering multiple metrics, resulting in… More >

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