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Context: Today's software industry is faced with rapidly changing requirements and high expectations from customers and users. Agile software development with a focus on rapid and frequent product deployment to end-users calls for equally continuous feedback acquisition, guiding software design and supporting evidencebased product development decisions. For keeping pace and succeeding on the highly competitive software market it is essential for companies to quickly and reliably evaluate their ideas based on real usage experiences in a natural context. This can be enabled using Continuous Experimentation (CE) - an approach to close the feedback cycle and tightly integrate the empirical evaluation of new features into the software development process. Objectives: With this project I aim at creating an understanding of CE as practiced and theorised today. Furthermore, my goal is to identify the smallest set of prerequisites for successfully implementing systematic experimentation practices into companies' software development process. I want to find solutions for companies to adopt CE as most suitable to their product stage, process maturity, and available resources; and enable organisations to systematically run experiments for data-driven decision-making. Methods: In addition to a currently on-going multivocal literature review, the project will involve empirical studies following a design science approach. Starting with a practitioner survey or interview, and a pilot case study for finding both, the typical hurdles encountered and the minimum requirements needed to apply CE in any company, the project will transition into an iterative multiple-case study, where proposed solutions are to be evaluated. Conclusions: The proposed doctoral research project will contribute to an increased understanding of systematic experimentation practices in different organisational settings, and strives for providing actionable solutions for enabling more flexible software development which can quickly react to feedback gained through experimentation, as well as an evaluation of these suggested solutions. Especially, this project aims at the creation of an easy-to-adopt approach called light-weight CE, which allows also companies with few resources or projects involving an earlystage product to benefit from the advantages of CE.

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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.

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