Decision Making in Software Project Management

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3079

Special Issue Editors


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Department of Computing and Games, Teesside University, Middlesbrough TS1 3BX, UK
Interests: IT project management; human–computer Interaction; digital healthcare; immersive technologies
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Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, Essex, UK
Interests: project management; data analysis; remote monitoring; machine learning

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Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: data science; network analysis; data mining; knowledge discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Effective decision making is at the core of successful software project management. In an era where software projects are becoming increasingly complex and dynamic, the ability to make informed, timely, and strategic decisions is critical for project success. This Special Issue explores the multifaceted nature of decision making in software project environments, focusing on the challenges, methodologies, and emerging trends that shape how decisions are made across different project stages.

The Special Issue will cover a range of topics, including risk management, resource allocation, leadership, stakeholder involvement, and the application of data-driven tools in decision making. It will also examine the role of decision making in both agile and traditional software project management methodologies, emphasizing the impact of these decisions on project outcomes.

By gathering cutting-edge research and case studies from academia and industry, this Special Issue aims to provide new insights into how decision making processes can be optimized to enhance software project success. Whether you are a project manager, software engineer, or researcher, the articles in this issue will offer valuable perspectives on navigating the complexities of decision making in modern software projects.

This Special Issue on Decision Making in Software Project Management welcomes empirical and literature review articles that aim to answer the following questions, among others:

  • How do decision making processes evolve in the context of modern software project management, especially in rapidly changing environments such as agile transformations or DevOps adoption?
  • What are the critical factors influencing effective decision making in managing software project risks, particularly in large-scale or distributed teams?
  • How can software project managers optimize risk management through data-driven decisions?
  • In the era of artificial intelligence and machine learning, how can software teams utilize AI-powered tools to support or automate decision making processes in software development, testing, and deployment?
  • What are the roles of leadership and stakeholder involvement in shaping key project decisions, and how do these factors impact software project success across different methodologies (e.g., Agile, Waterfall, Hybrid)?
  • What is the role of ethical decision making in software project management, especially in contexts like cybersecurity, data privacy, and AI-driven systems?
  • How do software project decisions impact social, environmental, and economic outcomes, particularly in sustainable software development practices?

Dr. Ikram Asghar
Dr. Rahmat Ullah
Dr. Farhan Amin
Guest Editors

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Keywords

  • software project management
  • decision making processes
  • digital transformation
  • risk management
  • project success factors
  • agile methodologies
  • leadership in software projects
  • data-driven decision making
  • project planning and control

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Published Papers (1 paper)

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Research

23 pages, 4723 KiB  
Article
AI-Driven Decision Support Systems in Agile Software Project Management: Enhancing Risk Mitigation and Resource Allocation
by Sultan Saaed Almalki
Systems 2025, 13(3), 208; https://doi.org/10.3390/systems13030208 - 18 Mar 2025
Viewed by 2493
Abstract
Agile software project management (ASPM) serves modern industries to conduct iterative development of complicated code bases. The decision-making process in Agile environments regularly depends on individual opinions, creating ineffective results for risk management and resource distribution. Artificial intelligence (AI) is a promising approach [...] Read more.
Agile software project management (ASPM) serves modern industries to conduct iterative development of complicated code bases. The decision-making process in Agile environments regularly depends on individual opinions, creating ineffective results for risk management and resource distribution. Artificial intelligence (AI) is a promising approach for handling these challenges by delivering data-based choices to project management. This research introduces an AI-based decision support system for improving risk reduction and resource distribution in ASPM. The system merges optimization frameworks and predictive analytics to enhance operational decision efficiency. The machine learning solution anchors data evaluation using AI models that simultaneously predict risks and strengthen decision power for resource scheduling. This analysis relied on project records and recent operational data to perform model validation and training procedures. Tests determined how the framework performed against contemporary Agile project management systems by measuring the completion speed of sprints, resource management practices, and risk prediction accuracy. The framework demonstrated better performance by predicting risks and simultaneously maximizing resources utilized during projects. The proposed framework outperformed traditional Agile applications, achieving 94% accuracy in risk identification and enhancing workload management by 25%, leading to an 18% improvement in sprint completion rates and overall project efficiency. These findings confirm that AI-driven decision support systems (DSSs) are crucial in enhancing Agile project management by enabling proactive risk mitigation and optimized resource allocation. By integrating AI-powered decision-making, the framework empowers organizations to improve project outcomes, streamline resource management, and facilitate the adoption of AI-driven methodologies within Agile systems. Full article
(This article belongs to the Special Issue Decision Making in Software Project Management)
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