eXtreme Programming, Agile Methodologies, Software Project Management, Customer Role, Rigorous Testing

Agile software development methodologies are attracting attention from academics and practitioners for planning and managing software projects. The eXtreme Programming (XP) challenges conformist wisdom regarding software system development processes and practices as agile methodologies. To work efficiently in the current software development practice, characterized by requirements fuzziness, XP moves away from document-centric operations into people-centric management. In the XP-based software project, the customers play an essential role, having multiple responsibilities such as driving the project, gathering requirements (‘user stories'), and exercising quality control (or acceptance testing). Besides, the customers must liaise with external project stakeholders (e.g., funding authorities, end-users) while maintaining the development team's trust and the wider business. The success of such software project management practices relies on the quality result of each stage of development obtained through rigorous testing. This chapter describes three characteristics of XP project management: customer role, software testing feedback, and learning.

The Mythical Lines of Code Metric

Using the lines of code (LOC) metric in software project management can be a financial moral hazard to an organization. This is especially true for upper management who handles an organizational budget and strategic plan. Software project managers have their own budgets. However, if they fail to meet the budget, the organization's cash flow, rather than the project manager's personal cash flow, will suffer. This chapter will discuss the practice of software project management, the field of software metrics, game theory, and the game theory issue of moral hazard. It will demonstrate why using LOC as a metric can present a moral hazard to senior management and an organization.

Multi-method Simulation Approach for Software Project Management Training in-process Decision Support System: a Conceptual-operative Framework

Software project management (SPM) is a discipline that comprises of different topics, practice and theories. There are two dimensions in the knowledge of SPM, theoretical or concepts of SPM and; practical or experience of SPM. These two dimensions although grow separately but come across at one point that is experiential knowledge of SPM. To present these dimensions through a proper training, a practitioner needs to have a proper view on SPM process. In this paper we present a new framework for practicing SPM through a distinct simulation technique to bring on an in-process decision support system, supporting the proper training approach. The framework developed based on three different methods of simulation technique, discrete event simulation (DES), system dynamics (SD) and partially observable Markov decision process (POMDP).1

Software Project Management Using Machine Learning Technique—A Review

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 results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.