Per Runeson

Per Runeson, Thomas Olsson, Johan Linåker

Software systems are increasingly depending on data, particularly with the rising use of machine learning, and developers are looking for new sources of data. Open Data Ecosystems (ODE) is an emerging concept for data sharing under public licenses in software ecosystems, similar to Open Source Software (OSS). It has certain similarities to Open Government Data (OGD), where public agencies share data for innovation and transparency. We aimed to explore open data ecosystems involving commercial actors. Thus, we organized five focus groups with 27 practitioners from 22 companies, public organizations, and research institutes. Based on the outcomes, we surveyed three cases of emerging ODE practice to further understand the concepts and to validate the initial findings. The main outcome is an initial conceptual model of ODEs' value, intrinsics, governance, and evolution, and propositions for practice and further research. We found that ODE must be value driven. Regarding the intrinsics of data, we found their type, meta-data, and legal frameworks influential for their openness. We also found the characteristics of ecosystem initiation, organization, data acquisition and openness be differentiating, which we advise research and practice to take into consideration.

Daniel Helgesson, Daniel Appelquist, Per Runeson

Objective: The purpose of this paper is to identify the largest cognitive challenges faced by novices developing software in teams. Method: Using grounded theory, we conducted an ethnographic study for two months following four ten person novice teams, consisting of computer science students, developing software systems. Result: This paper identifies version control and merge operations as the largest challenge faced by the novices. The literature studies reveal that little research appears to have been carried out in the area of version control from a user perspective. Limitations: A qualitative study on students is not applicable in all contexts, but the result is credible and grounded in data and substantiated by extant literature. Conclusion: We conclude that our findings motivate further research on cognitive perspectives to guide improvement of software engineering and its tools.

Qunying Song, Emelie Engström, Per Runeson

Testing of autonomous systems is extremely important as many of them are both safety-critical and security-critical. The architecture and mechanism of such systems are fundamentally different from traditional control software, which appears to operate in more structured environments and are explicitly instructed according to the system design and implementation. To gain a better understanding of autonomous systems practice and facilitate research on testing of such systems, we conducted an exploratory study by synthesizing academic literature with a focus group discussion and interviews with industry practitioners. Based on thematic analysis of the data, we provide a conceptualization of autonomous systems, classifications of challenges and current practices as well as of available techniques and approaches for testing of autonomous systems. Our findings also indicate that more research efforts are required for testing of autonomous systems to improve both the quality and safety aspects of such systems.

Florian Auer, Rasmus Ros, Lukas Kaltenbrunner et al.

Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. Method: We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. Results: The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. Conclusions: The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve.

Emelie Engström, Margaret-Anne Storey, Per Runeson et al.

Background: Assessing and communicating software engineering research can be challenging. Design science is recognized as an appropriate research paradigm for applied research but is seldom referred to in software engineering. Applying the design science lens to software engineering research may improve the assessment and communication of research contributions. Aim: The aim of this study is 1) to understand whether the design science lens helps summarize and assess software engineering research contributions, and 2) to characterize different types of design science contributions in the software engineering literature. Method: In previous research, we developed a visual abstract template, summarizing the core constructs of the design science paradigm. In this study, we use this template in a review of a set of 38 top software engineering publications to extract and analyze their design science contributions. Results: We identified five clusters of papers, classifying them according to their alignment with the design science paradigm. Conclusions: The design science lens helps emphasize the theoretical contribution of research output---in terms of technological rules---and reflect on the practical relevance, novelty, and rigor of the rules proposed by the research.

Ahmad Nauman Ghazi, Kai Petersen, Elizabeth Bjarnason et al.

Exploratory testing (ET) is a powerful and efficient way of testing software by integrating design, execution, and analysis of tests during a testing session. ET is often contrasted with scripted testing, and seen as a choice between black and white. We pose that there are different levels of exploratory testing from fully exploratory to fully scripted and propose a scale for the degree of exploration for ET. The degree is defined through levels of ET, which correspond to the way test charters are formulated. We have evaluated the classification through focus groups at four companies and identified factors that influence the level of exploratory testing. The results show that the proposed ET levels have distinguishing characteristics and that the levels can be used as a guide to structure test charters. Our study also indicates that applying a combination of ET levels can be beneficial in achieving effective testing.

Markus Borg, Emil Alégroth, Per Runeson

Software engineers working in large projects must navigate complex information landscapes. Change Impact Analysis (CIA) is a task that relies on engineers' successful information seeking in databases storing, e.g., source code, requirements, design descriptions, and test case specifications. Several previous approaches to support information seeking are task-specific, thus understanding engineers' seeking behavior in specific tasks is fundamental. We present an industrial case study on how engineers seek information in CIA, with a particular focus on traceability and development artifacts that are not source code. We show that engineers have different information seeking behavior, and that some do not consider traceability particularly useful when conducting CIA. Furthermore, we observe a tendency for engineers to prefer less rigid types of support rather than formal approaches, i.e., engineers value support that allows flexibility in how to practically conduct CIA. Finally, due to diverse information seeking behavior, we argue that future CIA support should embrace individual preferences to identify change impact by empowering several seeking alternatives, including searching, browsing, and tracing.