Mark Looi | Searcharxiv

arXiv2026

Outsourcing in Global Software Development: Effects of Temporal Location and Methodologies

Developing software globally using outsourced resources has become a common practice, with project teams often distributed in different time zones. In this study, we focus on customers that contract software development to vendors in temporally nearshore or far offshore locations. We conducted a survey to determine the effect of temporal distance on overall success, costs, project management effort, schedule, quality, communication problems, and other outcomes of interest to managers. In the survey of 80 customers and interviews with 6 of them, we also investigated the effect of software development methodology on the same outcomes. The results show that nearshore development is advantageous for overall success, quality, reduced PM effort, maintaining schedule, higher quality, and engendering fewer communication problems. Development methodology appears to only influence higher costs. We assess our findings in the context of prior GSE research and provide practical advice for customers of outsourced global software development, chief of which is to favor nearshore for communication-intensive or Agile projects.

cs.SE↗

arXiv2026

AI Tools in Software Development: Developer Perceptions and Usage Patterns

The use of Generative AI (GenAI) tools in software development has raised questions about their impact on productivity, code quality, and developer practices. Prior research presents mixed findings, with objective analyses identifying potential declines in code quality, while survey-based studies report perceived improvements in productivity and minimal quality trade-offs. This study presents an empirical analysis of survey data from 147 professional developers, examining associations between AI tool usage, perceived productivity, perceived code quality, and adoption intent. The results indicate that higher frequency and broader use of AI tools are associated with higher perceived productivity and perceived code quality. In contrast to concerns about a trade-off between speed and quality, developers report that these outcomes co-occur. Adoption intent is positively associated with current usage patterns and ease of integration, while security concerns show a modest negative association. AI tool usage in testing is less extensive than in coding, and perceived productivity gains are correspondingly lower. Clustering analysis identifies three developer segments-Enthusiasts, Pragmatists, and Cautious-which differ in usage breadth, perceived outcomes, and adoption intent. These patterns are consistent with diffusion-like models of technology adoption and suggest a reinforcing relationship between usage, perceived outcomes, and intended future use. These segments are also associated with differences in reported organizational context, including the presence of AI-related policies. Overall, the findings suggest that perceptions of productivity and quality, along with usage experience and integration factors, are associated with adoption behavior, highlighting the importance of considering both perceived and objective outcomes in evaluating AI-assisted software development.

cs.SE↗