Is Artificial Intelligence an Elixir to the Software Engineering Community? An Empirical Study among Managers

Expertise-building as the engine of advanced generative artificial intelligence utilization

The rapid advancement of near human Generative Artificial Intelligence (GAI) systems is reshaping the modern software engineering and development ecosystem, altering practitioners' workflows, cognitive processes, and technology engagement patterns. This research undertaking, through an empirical quantitative study, analyzed the structural drivers of Intended Use (IU) within a complex cognitive framework, challenging traditional technology acceptance paradigms that prioritize ease of use. Motivated by an industry rooted business problem of adoption variability in the fast-paced markets, competition and corporate dynamics, this study investigated the mechanisms through which software engineering and development practitioners build Expertise in GAI utilization and form Intentions to adopt GAI for algorithmic design and code generation and broader software engineering tasks. Thus, this study highlights the internalized logic--the mental model--that a developer builds when interacting with an AI. Grounded in extant theory and contextualized within business software engineering and development dynamics, the study examined how Familiarity, Trust, Perceived Usability, Perceived Behavioral Control, Deterrence, and Complexity influence GAI Expertise Building and Adoption decisions in the context of modern software engineering ecosystem. Therefore, this study intended to derive new findings and conclusions on what theory-based and industry factors contextualize and affect this process, with emphasis on the said ecosystem. The results revealed a highly efficient, parsimonious "engine" model. The negligible impact of deterrence (R²= 0.03) further underscored that user intent is driven by capability-based "pull" rather than an inhibitor-based "push". On a more granulated outlook, the major takeaway from this study is that although the predictors in our model would intuitively make sense individually, what I tried to unravel was to tell the story of the relative importance of each expected predictor within a parsimonious model, as well as the role of statistical controls in moderating these predictors. All predictors were anticipated to be important because we already know they affect the acceptance and adoption of other types of information technologies. The findings suggest that Expertise-Building serves as a sufficient proxy for predicting user behavior, as the action cannot be completed without the essential Expertise-Building "gatekeeper". GLM results in particular, emphasized that the "action" is a function of capability, not logistics. On an exploratory level, I also tested possible interaction among two important predictors, extending understanding of how adoption behaviors emerge in complex high-tech environments. Additional important implications, as well as study limitations and future research recommendations are also discussed. To enhance translational relevance, this study also addresses the practical challenges software engineering and development practitioners face during early-stage GAI adoption--particularly in enterprise grade software engineering contexts where GAI integration requires new models of learning, skill acquisition, trust calibration, and workflow adaptation. The findings offer actionable guidance and recommendations for developing corporate and organizational dynamic capabilities, such as GAI related learning curves, confidence in the utilization of GAI, and expertise development pathways. These insights support software engineers, development managers, R&D units, program leaders, and executive decision makers in designing GAI enabled workflows and methodologies, governance structures, and project strategies that enhance productivity, efficiency, and innovation across software manufacturing workflows. Collectively, the study contributes to both scholarly discourse and industry practice by illuminating how Expertise-Building in GAI technologies is cultivated and how such Expertise drives effective and confident Adoption in the software engineering and development ecosystem. Keywords: expertise-building of GAI utilization, GAI adoption for algorithm design, software engineering with AI, GAI for code building, trust, distrust, familiarity, perceived ease of use, perceived behavioral control, deterrence, complexity.

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

A shift from deterministic testing to artificial intelligence (AI) driven quality ecosystems is necessitated by the rapid evolution of software architectures, and research from 2016 to the present year is combined by this systematic literature review (SLR), so 195 main studies are analyzed, and the path of artificial intelligence in Software Quality Assurance (SQA) is mapped. 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 (LLMs) alongside autonomous agentic systems. In addition, three main areas are addressed by this study, and these areas are identified as the taxonomic shift toward multi-agent architectures, the functional effect of AI on labor-intensive activities such as regression testing, and self-healing automation, and the emerging social and technical challenges of ethical governance alongside explainability. Despite incomparable efficiency gains being offered by AI-driven techniques, the industrial success of these tools is strictly limited until the Maintenance Crisis of generated code is resolved and transparency is ensured through Explainable AI (XAI). Finally, the study concludes with a strategic roadmap for Ethical SQA, providing a foundation for future research in autonomous, self-evolving software systems.

The Role of AI in Application Development: A Comparative Study with Manual Coding

Application development has traditionally depended on manual coding, in which developers write every line of code themselves. This approach offers precision and control, but it is time-consuming, labour-intensive and prone to human error. The emergence of Artificial Intelligence (AI) has introduced tools that generate, test, debug and optimise code, raising the question of how AI-assisted development actually compares with manual practice. This paper presents a comparative study of the two approaches using secondary data from the Stack Overflow Annual Developer Survey 2024, comprising 65,437 responses from developers across 185 countries. Seven hypotheses were formulated covering productivity, job satisfaction, accuracy, compensation, challenges, sentiment and instrument reliability, and were tested using non-parametric methods (Mann-Whitney U, chi-square) at a 5% significance level. The job-satisfaction scale demonstrated excellent internal consistency (Cronbach's alpha = 0.931, 9 items, n = 29,095). The analysis found that 57.6% of respondents currently use AI tools, that 81.0% of adopters identify increased productivity as a benefit, and that 72.0% hold a favourable or very favourable view of AI. However, two widely assumed advantages did not survive testing. The difference in job satisfaction between AI users and manual coders was statistically significant but negligible in magnitude (means 6.97 vs 6.89; Cohen's d = 0.039). The apparent compensation advantage reversed direction once national context was controlled: pooled data showed manual coders earning more, yet within the United States alone the difference disappeared entirely (p = 0.203), indicating that the pooled gap is a confound arising from higher AI adoption in lower-income economies rather than an effect of AI itself. Trust remains the principal barrier, with 65.1% of respondents distrusting AI output and 61.9% reporting that AI tools lack context of their codebase. The study concludes that AI meaningfully augments developer productivity but does not yet demonstrably improve satisfaction or earnings, and that a hybrid human-AI model, supported by governance and training, remains the most defensible direction for application development

How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study

This paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face, and describes a seven-stage workflow and five process shifts, including a move toward evaluation-driven development.

How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests

This study characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle, offering a more nuanced understanding of their benefits and limitations in real-world practices.

Bachelor Computer Science & Economics