Petri Ihantola

Contributor turnover is a major challenge in open-source software (OSS) projects, affecting project sustainability, quality, and many other dimensions of project performance. Although turnover has been widely studied in the OSS context, the importance of sustained contribution periods to long-term contributor survival remains poorly understood. In this context, contributor survival refers to the probability of sustained activity over time. We investigate the effect of contribution streaks, defined as consecutive months of contribution, on long-term survival, and analyze what kind of engagement, at different levels of ownership, predicts short-term contribution streaks. Using five years of data from 14 open-source projects, we applied landmark analysis to understand long-term survival based on peak streak lengths and logistic regression to identify the activities supporting short-term streaks. In both cases, we identified a three-month contribution streak to be an important threshold for sustained contributions. We found that development and communication related activities across ownership levels are important predictors of whether contributors continue their streaks, even though the relative importance shifts over time. In addition, we found that prior experience plays a stronger role early on, but its effect diminishes during later streak months. These findings suggest that contribution streaks are important predictors of long-term survival and provide practical insights for open-source practitioners on how to improve the retention of new and existing contributors through supporting short-term contribution streaks.

The increasing adoption of hybrid and remote work has reshaped how software teams communicate, collaborate, and make design decisions. Our prior study showed that hybrid setups often result in missed discussions, fragmented understanding, and limited documentation of design rationale. At the same time, Generative AI (GenAI) tools–such as GitHub Copilot and ChatGPT–are becoming embedded in development workflows, providing support in code generation. While GenAI’s role in software development is gaining attention, its impact on early-stage design ideation activities and collaboration in hybrid teams remains underexplored. This paper builds on earlier findings to propose a forward-looking vision of GenAI as an instant, readily available co-developer in hybrid software design. We revisit documented challenges from our earlier study on hybrid collaboration and outline how GenAI could address these issues by facilitating asynchronous participation, surfacing undocumented rationale, and preserving design continuity. We present a conceptual framing of GenAI-supported collaboration and propose a research agenda to guide future studies on integrating GenAI into hybrid settings.

The future of Requirements Engineering (RE) is increasingly driven by artificial intelligence (AI), reshaping how we elicit, analyze, and validate requirements. Traditional RE is based on labor-intensive manual processes prone to errors and complexity. AI-powered approaches, specifically large language models (LLMs), natural language processing (NLP), and generative AI, offer transformative solutions and reduce inefficiencies. However, the use of AI in RE also brings challenges like algorithmic bias, lack of explainability, and ethical concerns related to automation. To address these issues, this study introduces the Human-AI RE Synergy Model (HARE-SM), a conceptual framework that integrates AI-driven analysis with human oversight to improve requirements elicitation, analysis, and validation. The model emphasizes ethical AI use through transparency, explainability, and bias mitigation. We outline a multi-phase research methodology focused on preparing RE datasets, fine-tuning AI models, and designing collaborative human-AI workflows. This preliminary study presents the conceptual framework and early-stage prototype implementation, establishing a research agenda and practical design direction for applying intelligent data science techniques to semi-structured and unstructured RE data in collaborative environments.

Vocational education suffers from high dropout rates. Motivation and well-being contribute to students’ dropout risk, yet they have been primarily studied at other educational levels. This study investigated what kinds of motivational groups (achievement goal orientation profiles) can be identified in vocational education (n = 782) and how differently motivated students differ regarding well-being and individualisation (emphasized in the vocational education reform). Latent profile analysis led to a typical four-class solution: mastery-oriented, success-oriented, indifferent, and avoidance-oriented students. Overall, vocational students showed rather high engagement and low burnout. Mastery- and success-oriented students were highly engaged in their studies, although the latter displayed some burnout symptoms. Avoidance-oriented students were the least engaged and experienced the highest levels of cynicism. Mastery- and success-oriented students were most interested in utilising individualised study paths. Thus, it would be essential to identify avoidant and disengaged students and to support also their motivation and well-being.

Requirement Engineering (RE) is the foundation of successful software development. In RE, the goal is to ensure that implemented systems satisfy stakeholder needs through rigorous requirements elicitation, validation, and evaluation processes. Despite its critical role, RE continues to face persistent challenges, such as ambiguity, conflicting stakeholder needs, and the complexity of managing evolving requirements. A common view is that Artificial Intelligence (AI) has the potential to streamline the RE process, resulting in improved efficiency, accuracy, and management actions. However, using AI also introduces new concerns, such as ethical issues, biases, and lack of transparency. This paper explores how AI can enhance traditional RE practices by automating labor-intensive tasks, supporting requirement prioritization, and facilitating collaboration between stakeholders and AI systems. The paper also describes the opportunities and challenges that AI brings to RE. In particular, the vision calls for ethical practices in AI, along with a much-enhanced collaboration between academia and industry professionals. The focus should be on creating not only powerful but also trustworthy and practical AI solutions ready to adapt to the fast-paced world of software development.