Artificial Intelligence Techniques to Enhance Cost, Effort, and Schedule Estimates in Software Development Projects: A Systematic Literature Review

Evaluation of Software Effort Estimation Methods for Machine learning techniques

Artificial Intelligence in Software System Quality Assurance: A Systematic Literature Review of Techniques, Tools, and Challenges (2016–2026)

An enormous course in scholarly output from 2023 onwards is revealed by the findings, and this growth is driven by the industrial adoption of Large Language Models alongside autonomous agentic systems.

Review of Supervised Machine learning cost estimation techniques for building projects

Cost is considered a vital parameter in determining the success of a construction project. Project costs control and monitoring prevent budget overruns and safeguard expected profits, regardless of the project's size, scope, or complexity. Traditional methods for estimating project costs are facing growing challenges as demand for more accurate, adaptable strategies that respond to evolving market dynamics and technological progress increases. This study offers insight into supervised ML-based cost estimation techniques, highlighting the models employed, the geographical area of the studies, sample sizes, input and output variables, and property types. The findings indicate that there has been some progress in applying supervised ML for cost estimation. Asia accounts for the most studies (65.96%), followed by Africa (10.64%) and Europe (14.89%). Oceania and North America each account for 4.26%, indicating a restricted research scope in these areas. Additionally, 62% of the studies employed multiple algorithms to enhance the reliability of the results. Moreover, most studies focused on construction costs rather than total project costs or total capital investment (project investment) and on residential and educational property types. The findings suggest that extensive testing and applications are necessary to gain a comprehensive understanding of global perspectives, particularly outside Asia, and in commercial properties such as retail and office buildings.

Machine Learning Models for Software Cost Estimation

Abstract Software cost estimation is a critical task in software projects development. It assists project managers and software engineers to plan and manage their resources. However, developing an accurate cost estimation model for a software project is a challenging process. This paper builds a software cost estimation model using machine learning approach. Different machine learning algorithms are applied to two public datasets to predict the software cost in the early stages. Results show that machine learning methods can be used to predict software cost with a high accuracy rate. Accurate software cost estimation remains one of the most critical challenges in software project management, directly influencing budgeting, resource allocation, scheduling, and overall project success. Traditional estimation techniques—such as expert judgment, algorithmic models (e.g., COCOMO), and function point analysis—often struggle with the increasing complexity, scale, and variability of modern software projects, leading to inaccurate predictions and cost overruns. This project explores the application of machine learning (ML) models to improve the accuracy and reliability of software cost estimation. Various ML techniques, including Linear Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and Artificial Neural Networks (ANN), are investigated and compared using historical software project datasets (such as COCOMO, Desharnais, or NASA datasets). Key project attributes—such as lines of code, function points, team experience, development environment, and project complexity—are used as input features to predict effort and cost outcomes. Data preprocessing techniques, including normalization, feature selection, and handling of missing values, are applied to enhance model performance. The performance of each model is evaluated using metrics such as Mean Magnitude of Relative Error (MMRE), Root Mean Square Error (RMSE), and Prediction Accuracy (PRED), enabling a comparative analysis to identify the most effective approach. Experimental results demonstrate that ensemble and neural network-based models generally outperform traditional statistical methods in terms of prediction accuracy and adaptability to non-linear relationships within project data. This project highlights the potential of machine learning as a robust, data-driven alternative to conventional cost estimation methods, offering improved decision-making support for project managers and stakeholders. Future work may focus on integrating deep learning architectures, real-time data streams, and hybrid models to further enhance estimation precision across diverse software development environments. Keywords: Software Cost Estimation, Machine Learning, Effort Prediction, COCOMO Model, Regression Analysis, Neural Networks, Random Forest, Support Vector Machine, Predictive Modeling, Software Project Management, Data Preprocessing, Feature Selection

Challenges and Contributions in Quality of AI-Based Software: A Systematic Mapping Study

A Systematic Mapping Study on the quality of AI-based software identifies six recurring challenge categories, with the most prominent being limitations in existing quality assessment models followed by issues in non-functional requirement management, quality-aware development, and quality assurance.

A Systematic Review of Artificial Intelligence for Software Requirements in Open-Source Software

Software stakeholders have seamlessly integrated artificial intelligence technologies into the requirements engineering processes for open-source software, which presents both opportunities and challenges. This systematic literature review examines the current state of artificial intelligence application in requirements engineering for open-source software and emphasizes the opportunities and challenges faced by practitioners. Based on the reviewed literature, artificial intelligence technologies, such as machine learning, natural language processing, large language models, and deep learning techniques, have been increasingly applied to support key requirements engineering activities, particularly requirements elicitation, classification, prioritization, and stakeholder analysis. However, the lack of structured data in open-source software directly challenges the effectiveness of artificial intelligence solutions for requirements engineering processes. The findings highlight the need for future research to focus on finding viable solutions to these identified issues. This review provides a consolidated overview for researchers and practitioners interested in enhancing artificial intelligence integration into requirements engineering for open-source software projects and provides guidance for future research to effectively address existing challenges and facilitate integration of AI techniques.