Vehid Geruslu

Vehid Geruslu, Alper Buğra Keleş, Sevde Değirmenci et al.

Objective: This study investigates the perceived value and critique of ISTQB certifications, the most widely recognized testing qualifications worldwide. While the certifications aim to standardize the software testing body of knowledge, debates persist about their practical relevance and impact. Our objective was to systematically capture practitioner perspectives and assess the precision of endorsements and fairness of criticisms through expert review. Method: We conducted an AI-assisted Multivocal Literature Review (MLR), combining academic and grey literature to synthesize practitioner endorsements (RQ1) and criticisms (RQ2). ChatGPT's deep research capability was employed under continuous human oversight, with QA strategies ensuring transparency and reliability. As another analysis, we asked a panel of four independent experts to evaluate the precision of endorsements and fairness of criticisms. Results: Practitioner endorsements emphasized career benefits, improved communication, and a shared vocabulary as the main values of ISTQB certifications. Criticisms focused on excessive theoretical content, limited relevance in agile and automation-intensive contexts, and weak support for real testing skills. Expert review confirmed that while many endorsements were precise, several criticisms reflected broader tensions in the discipline, including contrasting schools of thought in testing practice. Conclusions: ISTQB certifications provide recognizable career and communication value but remain contested in terms of practical utility. By triangulating practitioner voices with expert validation, this study delivers an evidence-based reflection on the strengths and weaknesses of ISTQB in shaping the software testing body of knowledge. The AI-assisted MLR also demonstrates how GenAI tools can support systematic evidence synthesis when coupled with rigorous human oversight.

Vehid Geruslu, Zulfiyya Aliyeva, Eray Tüzün

Context: The rapid adoption of AI-assisted code generation tools, such as large language models (LLMs), is transforming software development practices. While these tools promise significant productivity gains, concerns regarding the quality, reliability, and security of AI-generated code are increasingly reported in both academia and industry. --Objective: This study aims to systematically synthesize existing empirical evidence on the factors influencing the quality of AI-generated source code and to analyze how these factors impact software quality outcomes across different evaluation contexts. --Method: We conducted a systematic literature review (SLR) following established guidelines, supported by an AI-assisted workflow with human oversight. A total of 24 primary studies were selected through a structured search and screening process across major digital libraries. Data were extracted and analyzed using qualitative, pattern-based evidence synthesis. --Results: The findings reveal that code quality in AI-assisted development is influenced by a combination of human factors, AI system characteristics, and human AI interaction dynamics. Key influencing factors include prompt design, task specification, and developer expertise. The results also show variability in quality outcomes such as correctness, security, maintainability, and complexity across studies, with both improvements and risks reported. --Conclusion: AI-assisted code generation represents a socio-technical shift in software engineering, where achieving high-quality outcomes depends on both technological and human factors. While promising, AI-generated code requires careful validation and integration into development workflows.