Achieving project success is a primary objective for organizations dedicated to software development. Despite the concerted efforts of these organizations, statistics indicate that a significant portion of projects ultimately fail. Numerous authors in the literature have focused on defining success and failure in software projects, as well as the factors contributing to these outcomes. However, it appears that there is no consensus on these terms, and they do not accurately represent the experts’ perspective. This study presents results obtained from semi-structured interviews with 30 professionals in the technology sector, enabling the identification of Critical Success Factors that professionals believe can influence the success of software projects, along with their perception of the concepts of project success and project failure. The article outlines the conducted research, details the application of the thematic analysis methodology for analysing the interview data, and reports the findings of the empirical study. Specifically, the identification of 49 Critical Success Factors that experts deem influential in the success of software projects, providing a more nuanced and precise definition of project success and project failure. Among these 49 Critical Success Factors, grouped into six categories, it is important to highlight Skills, Involvement, Technology maturity, Detailed requirements analysis, and Good communication with all the people involved in the project, as the most frequently mentioned by the interviewees. The findings of this study not only provide a clearer definition of project success and project failure but also suggest that the resulting set of Critical Success Factors can support technology professionals in considering and adequately managing the factors that may increase the likelihood of success in their projects.

ABSTRACT Machine Learning (ML) development introduces challenges that traditional software processes often struggle to address. As ML applications grow in complexity and adoption, various lifecycle models have been proposed to address the unique stages of ML development. This study systematically synthesises these models, mapping their stages and activities to provide an understanding of the ML development landscape. The findings highlight research gaps and opportunities, offering insights for advancing academic research and practical implementation.

ABSTRACT Most software development organizations are project based. However, statistics show that the failure rate of projects is very high. Different authors have identified factors (critical success factors) that can influence the success or failure of software projects, and that must be considered when carrying out a software project. This study is part of a research aimed at defining a framework that allows software development companies to assess the extent of the impact of critical success factors on their projects and increase the probability of project success. To achieve this goal, the first step was to identify the factors influencing software project success as reported in recent literature, as presented in this paper. A systematic literature review was conducted to obtain the list of factors that can influence the success of software projects. The list of 50 critical success factors resulting from this literature review can be used as a guide of critical aspects to be taken into consideration by the project manager when managing a project. Several gaps were identified through the literature review, such as the lack of indicators to measure the level of impact of each factor and the absence of descriptions for these factors.