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Programmers' mental models represent their knowledge and understanding of programs, programming concepts, and programming in general. They guide programmers' work and influence their task performance. Understanding mental models is important for designing work systems and practices that support programmers. Although the importance of programmers' mental models is widely acknowledged, research on mental models has decreased over the years. The results are scattered and do not take into account recent developments in software engineering. We analyze the state of research into programmers' mental models and provide an overview of existing research. We connect results on mental models from different strands of research to form a more unified knowledge base on the topic. We conducted a systematic literature review on programmers' mental models. We analyzed literature addressing mental models in different contexts, including mental models of programs, programming tasks, and programming concepts. Using nine search engines, we found 3678 articles (excluding duplicates). 84 were selected for further analysis. Using the snowballing technique, we obtained a final result set containing 187 articles. We show that the literature shares a kernel of shared understanding of mental models. By collating and connecting results on mental models from different fields of research, we uncovered some well-researched aspects, which we argue are fundamental characteristics of programmers' mental models. This work provides a basis for future work on mental models. The research field on programmers' mental models still faces many challenges rising from a lack of a shared knowledge base and poorly defined constructs. We created a unified knowledge base on the topic. We also point to directions for future studies. In particular, we call for studies that examine programmers working with modern practices and tools., Comment: Submitted to Information and Software Technology

Background: Continuous experimentation (CE) has been proposed as a data-driven approach to software product development. Several challenges with this approach have been described in large organisations, but its application in smaller companies with early-stage products remains largely unexplored. Aims: The goal of this study is to understand what factors could affect the adoption of CE in early-stage software startups. Method: We present a descriptive multiple-case study of five startups in Finland which differ in their utilisation of experimentation. Results: We find that practices often mentioned as prerequisites for CE, such as iterative development and continuous integration and delivery, were used in the case companies. CE was not widely recognised or used as described in the literature. Only one company performed experiments and used experimental data systematically. Conclusions: Our study indicates that small companies may be unlikely to adopt CE unless 1) at least some company employees have prior experience with the practice, 2) the company's limited available resources are not exceeded by its adoption, and 3) the practice solves a problem currently experienced by the company, or the company perceives almost immediate benefit of adopting it. We discuss implications for advancing CE in early-stage startups and outline directions for future research on the approach., Comment: 23rd International Conference on Product-Focused Software Process Improvement (PROFES 2022)

Cognition plays a fundamental role in most software engineering activities. This article provides a taxonomy of cognitive concepts and a survey of the literature since the beginning of the Software Engineering discipline. The taxonomy comprises the top-level concepts of perception, attention, memory, cognitive load, reasoning, cognitive biases, knowledge, social cognition, cognitive control, and errors, and procedures to assess them both qualitatively and quantitatively. The taxonomy provides a useful tool to filter existing studies, classify new studies, and support researchers in getting familiar with a (sub) area. In the literature survey, we systematically collected and analysed 311 scientific papers spanning five decades and classified them using the cognitive concepts from the taxonomy. Our analysis shows that the most developed areas of research correspond to the four life-cycle stages, software requirements, design, construction, and maintenance. Most research is quantitative and focuses on knowledge, cognitive load, memory, and reasoning. Overall, the state of the art appears fragmented when viewed from the perspective of cognition. There is a lack of use of cognitive concepts that would represent a coherent picture of the cognitive processes active in specific tasks. Accordingly, we discuss the research gap in each cognitive concept and provide recommendations for future research.