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

Impact of artificial intelligence implementation in companies: a systematic review

Transforming Traditional Businesses Through Artificial Intelligence, Automation, and Data-Driven Strategies: A Systematic Review of Organizational Transformation, Operational Performance, and Competitive Advantage

The rapid developments in artificial intelligence (AI), intelligent automation, and data-driven decision-making have changed the way that competitive dynamics play out across industries and businesses, forcing them to rethink and reimagine their traditional working methods and key strategies. While there's an increase in investments in digital technologies, many organizations are still experiencing disparate implementations, legacy systems, organizational barriers, and inadequate data capabilities that lead to inconsistent transformation results. This study provides a systematic review of the academic sources to explore the role of the combination of AI, automation, and data-driven strategies in supporting the metamorphosis of conventional businesses and enhancing their operational efficiency, organizational agility, and long-term competitive advantage. A methodical literature review approach was used to present and synthesize peer-reviewed studies from the main academic databases according to specific inclusion and exclusion criteria to guarantee methodological rigor and transparency. The review brings together insights from various industries, including manufacturing, retail, healthcare, finance, logistics, and small and medium-sized businesses, to find out what technological capabilities all have in common in these industries, what challenges they encounter when implementing them, what enablers they require at the organizational level and what measurable business outcomes they achieve. Evidence synthesized suggests that digital transformation is not just about technology, but also about investing in complementary aspects such as organizational capabilities, leadership commitment, workforce reskilling, process redesign, and strong data governance. The adoption and integration of AI into intelligent automation and evidence-based decision-making consistently leads to increased productivity, cost savings, customer satisfaction, operational resilience, and innovation capabilities - which, however, is heavily dependent on the ability of the organization to implement AI and become digitally mature. From these insights, this review suggests an integrated conceptual model linking technological, organizational and data capabilities and business transformation outcomes. The study advances the digital transformation literature by offering a comprehensive, evidence-based synthesis that is able to bridge between the fragmented research streams and provide recommendations for managers, policy makers, and researchers aiming at accelerating sustainable transformation using AI in traditional business settings.

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

Artificial Intelligence (AI) is increasingly embedded in modern software systems, raising important questions about how its quality should be defined, assessed, and assured. This paper presents a Systematic Mapping Study (SMS) on the quality of AI-based software. The study synthesizes primary studies published between January 2020 and January 2026 and selected from five electronic data sources. A total of 33 primary studies were included after automated search, screening, and snowballing. The results identify 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. The findings suggest a call for collaboration of researchers and industrial practitioners with standardization organizations, that could possibly devise comprehensive quality assessments and their measurement methods.

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

The inaccuracy of cost, effort, and schedule estimates remains one of the primary factors associated with failures in software development projects, particularly in contexts characterized by high complexity and frequent changes in requirements. Given the well‐documented limitations of traditional estimation methods, this study aims to systematically analyze how artificial intelligence (AI) techniques have been applied to improve the accuracy of such estimates in software projects. To this end, a rigorous systematic literature review (SLR) was conducted, structured according to the PICOC protocol and established guidelines for systematic reviews, encompassing searches in the IEEE Digital Library, ACM Digital Library, SpringerLink, and ScienceDirect. In total, 108 primary studies published between 2015 and 2025 were analyzed, selected based on predefined inclusion and exclusion criteria as well as methodological quality assessment. The findings indicate that techniques such as artificial neural networks, optimization algorithms, machine learning models, and hybrid approaches consistently yield improvements in estimation accuracy, with average error reductions reported in the literature ranging approximately from 15% to 30% when compared with traditional methods. The reviewed studies also highlight challenges related to data quality and availability, model reproducibility, and the feasibility of deploying these approaches in real‐world environments. As a contribution, this SLR provides a structured synthesis of the state of the art, identifies research gaps, and offers valuable insights for both the academic community and industry practitioners in the development of more accurate and reliable estimation models and tools.

Implementing agentic AI in project management: an evidence-based framework and maturity model from systematic analysis

This study develops an evidence-based implementation framework and maturity model for organizations adopting agentic artificial intelligence (AI) in project management, synthesizing insights from academic research and industry implementations. Following PRISMA 2020 guidelines, we systematically reviewed 97 sources spanning 2019–2025, including 52 peer-reviewed articles and 45 rigorously screened gray literature sources (industry reports, technology documentation and professional body publications) across construction, software development and other sectors. We identify five levels of AI autonomy currently deployed in practice, three dominant implementation pathways and critical success factors including data infrastructure readiness, organizational change capacity and governance mechanisms. Organizations report 30–50% productivity improvements, though quantified cost-reduction data remain notably absent from the published literature, suggesting either competitive sensitivity or measurement challenges in this emerging field. The evidence base is dominated by recent publications (65% from 2024–2025) and gray literature (41%), which limits the generalizability and durability of the findings. Quantified cost-reduction data specific to agentic AI are entirely absent from published sources. Future research should prioritize controlled empirical studies comparing agentic and traditional project management approaches, longitudinal tracking of human–AI team evolution, cross-cultural adoption studies and development of standardized reporting frameworks for agentic AI implementations. The study provides: (1) a maturity assessment tool for organizational readiness, (2) an implementation roadmap with phase-gates and risk mitigation strategies, (3) a governance framework for human–AI collaboration and (4) an organizational performance measurement framework with validated Key Process Indicators (KPIs). This is the first comprehensive analysis specifically focused on agentic AI in project management, providing actionable frameworks tested across multiple industries. The maturity model and implementation pathways offer immediate business value for organizations navigating AI transformation.

The Symbiosis of Formal Methods and Artificial Intelligence

Formal methods are software engineering approaches with a rigorous mathematical basis that can help ensure the correctness of software systems, especially where safety or security is critical. Artificial Intelligence (AI) has developed very rapidly in the area of Generative AI (GenAI), where questions can be answered with increasingly impressive but potentially unreliable and variable results. This paper surveys research in integrating the two approaches in a synergistic manner. Traditionally, such explorations have required significant manual efforts in searching for and evaluating existing research. However, most relevant publications are now accessible online, and AI tools are increasingly good at answering research questions with more and more reliability. This paper takes the approach of using GenAI to evaluate research questions on combining formal methods and AI-related techniques. The paper assesses the usefulness and validity of these results. With recent improvements in AI search tools, the approach is now a useful aid to researchers, significantly reducing the time needed to survey existing research, while always needing human checking by an expert. In addition, the combination of formal methods and AI approaches looks to be an interesting and beneficial research area with potential industrial-scale applications in the future. This paper does not claim to be an exhaustive systematic review, but rather demonstrates an AI-assisted search process to aid in literature surveys within a fast-moving research area.