Puguh Jayadi, Didik Dwi Prasetya, Triyanna Widiyaningtyas
This study conducts a systematic literature review (SLR) to assess the state-of-the-art in Software Effort Estimation (SEE), a capability that underpins effective software project management. Reliable effort estimates have a direct influence on budget allocation, resource planning, and delivery schedules, making progress in SEE both practically and scientifically significant. The review’s primary objective is to identify current trends, surface emerging challenges, and delineate opportunities for advancing research in SEE. Following the structured guidelines of Kitchenham (2007), the review applies a transparent protocol for study identification, selection, and synthesis, resulting in a curated set of 87 studies that collectively inform the research questions. Results indicate that CI-based techniques have gained prominence in recent SEE literature, while machine learning and artificial neural networks are the most adopted approaches. The popularity of these methods can be partly explained by their ability to model complex nonlinear relationships among cost drivers and project attributes. However, key challenges persist. Issues related to the availability and quality of datasets are at the top: limited representativeness, inconsistency, and noise limit comparability across studies and reduce external validity. The trade-off between the accuracy of a model and its interpretability remains another pressing challenge faced by practitioners in the field, since highly accurate "black-box" models are often hard to explain and make part of a decision. As stated, the contribution of future studies in assembling more representative datasets and incorporating other AI technologies, including deep learning and natural language processing, is required to further improve the modeling capabilities of SEE. Such improvements are likely to contribute to making estimation methods more robust, scalable, and interpretable, which in turn would advance planning and execution for software projects. © 2025 by the authors.
Department of Informatics, Faculty of Engineering, Universitas PGRI Madiun, Indonesia; Department of Electrical Engineering and Informatics, Faculty of Engineering, Universitas Negeri Malang, Indonesia
