arXiv subjects

Zafar Jafarov

Publications and source records attributed to Zafar Jafarov.

5 recordsLinked to original sources

arXiv2026

Implementing GenAI-Supported Learning in Software Engineering and Computer Science Education using Bloom's Taxonomy

Context: Generative AI adoption in software engineering education raises opportunities for learning support alongside concerns about superficial learning and academic integrity. Objective: This study investigates how explicit instructional guidance aligned with Bloom's taxonomy supports responsible GenAI use in SE/CS education, exploring student and instructor perceptions of GenAI-supported learning. Method: We designed a Bloom-aligned GenAI framework that articulated appropriate GenAI roles at different cognitive levels. The framework was embedded in course instructions, labs, and assessment across multiple SE/CS courses at Queen's University Belfast and Azerbaijan Technical University. Data were collected via anonymous questionnaires and learning artifacts, analyzed using thematic analysis with Bloom's taxonomy as an analytic lens. Results: Students perceived GenAI as most valuable for higher-order cognitive activities (analysis, evaluation, reflection) and less suitable for foundational learning. Explicit Bloom-level guidance influenced students to use GenAI reflectively, with delayed or intentional non-use when independent thinking was prioritized. Both students and instructors reported pedagogical benefits alongside challenges in cognitive effort and instructional design workload. Conclusion: GenAI's educational value lies in intentional alignment between cognitive learning goals, instructional guidance, and learner self-regulation. Bloom's taxonomy provides a scalable, pedagogy-driven framework for responsible GenAI use in SE/CS education, offering a practical alternative to enforcement-focused responses.

cs.SE↗

arXiv2025

How Software Engineers Engage with AI: A Pragmatic Workflow

Artificial Intelligence (AI) tools such as GitHub Copilot and ChatGPT are increasingly used in software engineering (SE) for tasks such as code, test, and documentation generation. However, engineers often face uncertainty about when to trust, refine, or discard AI-generated artifacts. We present a pragmatic workflow, complemented by a four-quadrant decision model, that formalizes how developers iteratively prompt, inspect, refine, and, when needed, fall back to manual work. The workflow and decision model were derived from a grey literature review and field observations across three industrial settings in T\"urkiye and Azerbaijan. Two real-world scenarios demonstrate the workflow's practical value, showing how engineers navigate key decision points when using AI. Our approach offers lightweight, structured guidance to support more deliberate and quality-aware use of AI tools in everyday SE tasks.

cs.SE↗

arXiv2025

MBTModelGenerator: Automated Reverse Engineering of Test Models from Clickstream Data for Model-Based Testing of Web Applications

Context: Model-Based Testing (MBT) was first introduced in 1970's, and has the potential to improve efficiency and effectiveness of testing. However, its adoption-especially for web applications-has been hindered by the effort required to manually design MBT models, and keep them updated. Objective: Based on the above challenge in a real industrial context, this study introduces an automated approach to reduce that effort by reverse engineering MBT models from clickstream data captured during users' interaction with web applications. Method: We have developed and present in this paper an open-source tool, named MBTModelGenerator, which logs user interactions via a lightweight JavaScript module in the front-end, and transmits them to a REST API backend. These interactions are then transformed into directly executable MBT models in the input format of an open-source MBT tool named GraphWalker. Results: The tool was evaluated on two representative open-source web applications, Spring PetClinic and a Task Manager web app, and is under evaluation in several large-scale industrial testing projects. The generated MBT models accurately reflected user navigation flows and could be executed in the GraphWalker MBT tool without any manual changes. Using the tool has significantly reduced the effort of MBT model design by more than 90%, while still allowing test engineers to inspect and refine the generated models for completeness. Conclusion: Our approach facilitates lightweight adoption of MBT by automating model generation, which is the most effort intensive phase of MBT. To ensure correctness and completeness, the generated models should still be reviewed by test engineers -- but that effort remains substantially lower than designing MBT models from scratch. The tool is in active industrial use and available as open-source for reuse and further development.

cs.SE↗

arXiv2025

Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications

Context: As generative AI (GenAI) tools such as ChatGPT and GitHub Copilot become pervasive in education, concerns are rising about students using them to complete rather than learn from coursework-risking overreliance, reduced critical thinking, and long-term skill deficits. Objective: This paper proposes and empirically applies a causal model to help educators scaffold responsible GenAI use in Software Engineering (SE) education. The model identifies how professor actions, student factors, and GenAI tool characteristics influence students' usage of GenAI tools. Method: Using a design-based research approach, we applied the model in two contexts: (1) revising four extensive lab assignments of a final-year Software Testing course at Queen's University Belfast (QUB), and (2) embedding GenAI-related competencies into the curriculum of a newly developed SE BSc program at Azerbaijan Technical University (AzTU). Interventions included GenAI usage declarations, output validation tasks, peer-review of AI artifacts, and career-relevant messaging. Results: TBD Conclusions: TBD

cs.SE↗

arXiv2025

AI-powered software testing tools: A systematic review and empirical assessment of their features and limitations

Context: The rise of Artificial Intelligence (AI) in software engineering has led to the development of AI-powered test automation tools, promising improved efficiency, reduced maintenance effort, and enhanced defect-detection. However, a systematic evaluation of these tools is needed to understand their capabilities, benefits, and limitations. Objective: This study has two objectives: (1) A systematic review of AI-assisted test automation tools, categorizing their key AI features; (2) an empirical study of two selected AI-powered tools on two software under test, to investigate the effectiveness and limitations of the tools. Method: A systematic review of 55 AI-based test automation tools was conducted, classifying them based on their AI-assisted capabilities such as self-healing tests, visual testing, and AI-powered test generation. In the second phase, two representative tools were selected for the empirical study, in which we applied them to test two open-source software systems. Their performance was compared with traditional test automation approaches to evaluate efficiency and adaptability. Results: The review provides a comprehensive taxonomy of AI-driven testing tools, highlighting common features and trends. The empirical evaluation demonstrates that AI-powered automation enhances test execution efficiency and reduces maintenance effort but also exposes limitations such as handling complex UI changes and contextual understanding. Conclusion: AI-driven test automation tools show strong potential in improving software quality and reducing manual testing effort. However, their current limitations-such as false positives, lack of domain knowledge, and dependency on predefined models-indicate the need for further refinement. Future research should focus on advancing AI models to improve adaptability, reliability, and robustness in software testing.

cs.SE↗