Project management is an important factor to accomplish the decision to implement large-scale Software systems (LSS) in a successful manner. The effective Project management comes into play to plan, coordinate and control such a complex Project. Project management factor has been argued as one of the important Critical Success Factor (CSF), which need to be measured and monitored carefully during the implementation of Enterprise Resource Planning (ERP) systems. The goal of this article is to develop â AœCSF-Live! â AI which is a method for measuring, monitoring, and controlling critical success factors of large-scale Software systems. To achieve such goal, we apply CSF-Live for the Project management CSF. The CSF-Live uses the Goal/Question/Metric paradigm (GQM) to yield a flexible framework containing several metrics that we used it to develop a formulation to enable the measurement of the Project management CSF. The formulation that we developed for the Project management CSF implies that the significance of having proper Project management when conducting an ERP system implementation, since it is positively associated with the success of the ERP.

The Software effort estimation plays an important role in Software Project management, and analogy-based estimation (ABE) is the most common method used for this purpose. ABE estimates the effort required for a new Software Project based on its similarity to the previous Projects. A similarity between the Projects is evaluated based on a set of Project features, each of which has a particular effect on the degree of similarity between the Projects and the effort feature. The present study examines the hybrid PSO-SA approach for feature weighting in the analogy-based Software Project effort estimation. The proposed approach is implemented and tested on two well-known datasets of Software Projects. The performance of the proposed model is compared with the other optimization algorithms based on the MMRE, MDMRE, and PRED (0. 25) measures. The results obtained showed that the weighted ABE models provide more accurate and better effort estimates relative to the unweighted ABE models and that the hybrid PSO-SA approach leads to better and more accurate results compared to the other weighting approaches in both datasets.

Project managers analyze the factors that affected Projects' success, signifying performing a Project within the scopes (Time, Quality, and cost) defined in the initial step. The implication of each factor on Project success is essential since several of them have been specified in this area. Employing all of them is not feasible, and it may impose outrageous expenses on the organizations. Therefore, this article aims to identify the factors that impact Project accomplishment and pinpoint the most contributing factors to facilitate the Project's implementation. The main contribution of this paper is representing a framework by combining Machine Learning algorithms and simulation models to detect the effectiveness of leading organizational factors on Project accomplishment, beneficially leading to extracting the accurate analysis.   A logistic regression algorithm was employed to build a predictive model. The predictive model was created based on independent variables to predict whether the Software Project would be successful or fail. Also, Gamification was determined as the most influential factor on the objective by the Logistic regression feature importance method. Then, Gamified and non-Gamified models were compared by the Simulation method and showed Gamification made a 36.26% improvement in the rojects cycle time and a 15% enhancement in the quality of employers' performance by decreasing the Projects' bugs. For validating the simulation results, some Projects were implemented in the real case study, and the model results clarified the Gamification potential in improving employee engagement leading to better work progress tracking and higher performance quality.

Inefficient Project planning and control have been identified as the main contributor to the reduced performance of the construction industry in Iran. Meanwhile, improper use of planning and control Software packages (PPCSPs) in these Projects can be a key factor in this reduced performance. This study investigates different aspects of the PPCSP applications to draw the role of PPCSPs in the planning and control processes of construction Projects in the country using a survey-based method. It is found that only 32.5% of the construction companies in Iran highly or very highly use PPCSPs. The low level of skill and the lack of management support are two main contributing factors to this reduced PPCSP application. The quality of the academic and vocational Project management training programs and the lack of dependable PPCSP technical support are argued as possible sources of the issue. The identified PPCSP pattern in Iran is compared with the results reported for several developed and in-transition countries. This comparison reveals that Iran and in-transition countries fall deeply behind in employing PPCSPs in their construction Projects compared to the developed countries.