Machine Learning and Data Science Project Management From an Agile Perspective: Methods and Challenges (original) (raw)

Towards an Intelligent Machine Learning-based Business Approach

International Journal of Intelligent Systems and Applications, 2022

With the constant increase of data induced by stakeholders throughout a product life cycle, companies tend to rely on project management tools for guidance. Business intelligence approaches that are project-oriented will help the team communicate better, plan their next steps, have an overview of the current project state and take concrete actions prior to the provided forecasts. The spread of agile working mindsets are making these tools even more useful. It sets a basic understanding of how the project should be running so that the implementation is easy to follow on and easy to use. In this paper, we offer a model that makes project management accessible from different software development tools and different data sources. Our model provide project data analysis to improve aspects: (i) collaboration which includes team communication, team dashboard. It also optimizes document sharing, deadlines and status updates. (ii) planning: allows the tasks described by the software to be us...

Software Project Management Using Machine Learning Technique—A Review

Applied Sciences

Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the r...

Combining Machine Learning and Operations Research Methods to Advance the Project Management Practice

Operations Research and Enterprise Systems, 2019

Project Management is a complex practice that is associated with a series of challenges such as handling of conflicts and dependencies in resource allocation, fine tuning of projects to avoid fragmented planning, handling of potential opportunities or threats during the execution of a project, and alignment between projects and business objectives. Traditionally, methods and tools to address these issues are based on analytical approaches developed in the realm of the Operations Research discipline. Aiming to facilitate and augment the quality of the Project Management practice, this paper proposes a hybrid approach that builds on the synergy between contemporary Machine Learning and Operations Research techniques. Based on past data, Machine Learning techniques can predict undesired situations, provide timely warnings and recommend preventive actions regarding problematic resource loads or deviations from business priority lists. The applicability of our approach is demonstrated through two real examples elaborating two different datasets. In these examples, we comment on the proper orchestration of the associated Operations Research and Machine Learning algorithms, paying equal attention to both optimization and big data manipulation issues.

On the Advancement of Project Management through a Flexible Integration of Machine Learning and Operations Research Tools

Proceedings of the 8th International Conference on Operations Research and Enterprise Systems, 2019

Project Management is a complex practice that is associated with a series of challenges to organizations and experts worldwide. Aiming to advance this practice, this paper proposes a hybrid approach that builds on the synergy between contemporary Machine Learning and Operations Research tools. The proposed approach integrates the predictive orientation of Machine Learning techniques with the prescriptive nature of Operations Research algorithms. It can aid the planning, monitoring and execution of common PM tasks such as resource allocation, task assignment, and task duration estimation. The applicability of our approach is demonstrated through two realistic examples.

Role of Machine Learning Techniques in Cost Prediction of Agile Projects

Zenodo (CERN European Organization for Nuclear Research), 2023

In current scenario of software industry culture, an important and crucial task under project management is accurate estimation of practical measures like cost and effort which subsequently results in successful project completion. Many researchers have analysed and proposed various techniques in the estimation for software projects using conventional frameworks like waterfall, incremental etc. In recent years as there are technological advancements and there is a requirement of adaptation to technological changes, hence agile development methodology has attracted the interest of many researchers and software developers in software companies. Various researchers have proposed several techniques including opinion based, algorithm based and machine learning based techniques for effort and cost estimation of software projects. The proposed work in this paper deals with the study and analysis of the most popular techniques used in every category of estimation practices. In current scenario of software industry culture, an important and crucial task under project management is accurate estimation of practical measures like cost and effort which subsequently results in successful project completion. Many researchers have analysed and proposed various techniques in the estimation for software projects using conventional frameworks like waterfall, incremental etc. In recent years as there are technological advancements and there is a requirement of adaptation to technological changes, hence agile development methodology has attracted the interest of many researchers and software developers in software companies. Various researchers have proposed several techniques including opinion based, algorithm based and machine learning based techniques for effort and cost estimation of software projects. The proposed work in this study deals with the study and analysis of the most popular techniques used in every category of estimation practices used in agile development. This paper describes a review of the research work conducted in the effort estimation of non-agile and agile projects in the previous years, which consists of different techniques and approaches. The first section describes an introduction of the relevance of estimation in project management, the second section covers a study of all those researches which have analysed the use of various machine learning based techniques for estimation of software projects and in the third section a comparative analysis is done based on the studied literature in terms of

The Nexus between the Machine Learning Techniques and Software Project Estimation

Global Disclosure of Economics and Business

Machine Learning is an application of artificial intelligence that allows computers to learn and develop without explicit programming. In other words, the goal of ML is to let computers learn on their own without human involvement and then alter their activities. ML also allows huge data processing. Project management planning and evaluation are vital in project execution. Project management is difficult without a realistic and logical plan. We give a complete overview of works on Machine Learning in Software Project Management. The first category contains software project management research articles. The third category includes research on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in production, and promotion of machine-learning project prediction. Our contribution also provides a broader viewpoint and context for future project risk management efforts. In conclusion...

Estimation Approaches of Machine Learning in Scrum Projects: A Review

IJRASET, 2021

It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx 43% of the projects are often delivered late and entered crises because of over budget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g. Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon for strengthening the collaboration between developer and customer. Estimation has always been challenging in Agile as requirements are volatile. This encourages researchers to work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paper reviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation.

AI-Powered Project Management: Myth or Reality? Analyzing the Integration and Impact of Artificial Intelligence in Contemporary Project Environments

International Journal of Applied Engineering & Technology , 2024

The adoption, effects, and difficulties of artificial intelligence (AI) in modern project environments are addressed in this article under the increased interest in incorporating AI into project management techniques. We carried out a thorough investigation using statistical methods since we felt the need to comprehend the changing role of AI in project management. We want to learn more about the kinds of AI apps being used, how well they improve project results, and what obstacles stand in the way of their seamless integration. To this end, we distributed questionnaires and held interviews with project management professionals from a variety of industries. Predictive analytics, natural language processing, and machine learning are becoming common tools in project management, according to our research, which shows that the use of AI technologies is growing. In addition, our study shows that AI has a very good impact on decision-making procedures, cost reductions, and project efficiency. However, issues like organizational reluctance, expertise shortages, and data privacy concerns have been noted as barriers to the mainstream adoption of AI in project management. By illuminating these fundamental ideas, our research advances knowledge of the dynamics surrounding the integration of AI in project management and offers insightful advice to companies looking to make use of the revolutionary potential of AI technology in their initiatives.

The Impact of Artificial Intelligence on Project Managers and Scrum Masters: A review and evaluation study

https://ssrn.com/abstract=5049345, 2024

Artificial intelligence has taken a central role in various industries in the past decade as the importance of data has been at the forefront of all business decisions and policies. However, the increasing introduction of AI is proposed to alter entire project management enterprises as online platforms and applications have arisen, providing users with AI emotional intelligence, project management, and organizational tools. Bots are able to create reports, provide analysis, and facilitate headway by generating prioritized tasks and delegating to individuals through teamwork recommendation engines. However, the potential for AI to completely automate project management and Scrum Master tasks and remove job opportunities has yet to be comprehensively discussed. (Auth et al.2021)(Najdawi and Shaheen2021)(Josyula et al.2023

Machine Learning: A Software Process Reengineering in Software Development Organization

International Journal of Engineering and Advanced Technology, 2019

BPR (Business Process Re-engineering) is an organizational mechanism that improves the organizational ability in responding to the challenges of qualitative result by change management and improvement in software engineering processes, productivity, product quality and competitive advantage. BPR inherits, explores and implements the building of process change, to incorporate enhancements to the essential considerations and protocols of (SEM) Software Engineering Management. Machine Learning (ML) can be the key aspect for BPR in software development organization. The goal of this research study is raising the conceptual vision about integration of automation technology like ML and its life cycle development within Software Development Life Cycle (SDLC) of the software product and highlights benefits and drawbacks ML techniques in SPM (Software Project Management), and how to implement ML in standard SEM practices. We have attempted the introduction of machine learning in SEM to deter...