Requirements engineering in open innovation: a research agenda

In recent years Open Innovation (OI) has gained much attention and made firms aware that they need to consider the open environment surrounding them. To facilitate this shift Requirements Engineering (RE) needs to be adapted in order to manage the increase and complexity of new requirements sources as well as networks of stakeholders. In response we build on and advance an earlier proposed software engineering framework for fostering OI, focusing on stakeholder management, when to open up, and prioritization and release planning. Literature in open source RE is contrasted against recent findings of OI in software engineering to establish a current view of the area. Based on the synthesized findings we propose a research agenda within the areas under focus, along with a framing-model to help researchers frame and break down their research questions to consider the different angles implied by the OI model

Open innovation using open source tools: a case study at Sony Mobile

Despite growing interest of Open Innovation (OI) in Software Engineering (SE), little is known about what triggers software organizations to adopt it and how this affects SE practices. OI can be realized in numerous of ways, including Open Source Software (OSS) involvement. Outcomes from OI are not restricted to product innovation but also include process innovation, e.g. improved SE practices and methods. This study explores the involvement of a software organization (Sony Mobile) in OSS communities from an OI perspective and what SE practices (requirements engineering and testing) have been adapted in relation to OI. It also highlights the innovative outcomes resulting from OI. An exploratory embedded case study investigates how Sony Mobile use and contribute to Jenkins and Gerrit; the two central OSS tools in their continuous integration tool chain. Quantitative analysis was performed on change log data from source code repositories in order to identify the top contributors and triangulated with the results from five semi-structured interviews to explore the nature of the commits. The findings of the case study include five major themes: i) The process of opening up towards the tool communities correlates in time with a general adoption of OSS in the organization. ii) Assets not seen as competitive advantage nor a source of revenue are made open to OSS communities, and gradually, the organization turns more open. iii) The requirements engineering process towards the community is informal and based on engagement. iv) The need for systematic and automated testing is still in its infancy, but the needs are identified. v) The innovation outcomes included free features and maintenance, and were believed to increase speed and quality in development. Adopting OI was a result of a paradigm shift of moving from Windows to Linux

Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision

The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature’s themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms

Reshaping Sustainable University Education in Post-Pandemic World : Lessons Learned from an Empirical Study

The outbreak of COVID-19 has affected people all around the world. Governments had no choice but to put people in self-isolation to stop the spread of the virus. As a result, all companies and educational institutions switched to working or studying from home. The purpose of the study is to investigate the impact of COVID-19 on student teaching and learning in the context of Malmo university. Furthermore, the study proposes recommendations for sustainable post-pandemic education at Malmo University. The study includes ten semi-structured interviews with students followed by a workshop with ten senior lecturers teaching bachelor's and master's courses. The study uses snowball sampling to select students for the interviews and senior lecturers for the workshop. A qualitative data analysis technique, thematic analysis, is used for data analysis on the data collected from interviews with students and the workshop with senior lecturers. The results from the study suggested that online education leads to several benefits for students, such as better time management, higher lecture attendance, flexibility, and discipline in their studies. However, the shift to online education has caused a communication deterioration between students and teachers. Less social interaction with other students leads to depression, anxiety, and stress. The recommendations for post-pandemic education include the unified selection of digital learning tools across courses, a designated budget for digital learning tools, training support, and hybrid learning methods. In conclusion, the study proposes blended and hybrid learning to improve higher education at the university, requiring digital tools to minimize students' communication barriers

Doctoral education process and product using constructive alignment in software engineering and computer science

Sweden is seen as one of the most research-driven and educated countries in the world. Thus, Doctoral education is considered one of Sweden's most important parts of higher education. This position paper reflects upon the process and product of doctoral education in Computer Science and Software Engineering in Sweden. The paper provides an overview of doctoral education in Sweden, followed by a practical demonstration of how supervisors and doctoral students could use constructive alignment to achieve the learning outcomes of doctoral education using learning activities and assessment methods to evaluate the learning activities and, by extension, the learning outcomes.Sverige ses som ett av de mest forskningsdrivna och utbildade länderna i världen. Forskarutbildningen anses därmed vara en av Sveriges viktigaste delar av den högre utbildningen. Detta ställningstagande reflekterar över forskarutbildningens process och produkt i Datavetenskap och programvarusystem i Sverige. Artikeln ger en översikt över forskarutbildningen i Sverige, följt av en praktisk demonstration av hur handledare och doktorander kan använda konstruktiv anpassning för att uppnå lärandemålen för forskarutbildningen med hjälp av inlärningsaktiviteter och bedömningsmetoder för att utvärdera lärandeaktiviteterna och i förlängningen lärandemålen.

An Empirically Based Theory for Open Software Engineering Tools

Many companies and developers from OSS communities create open tools collaboratively in which software developers improve upon the code and share the changes within the community. Open tools (e.g., Jenkins, Gerrit, and Git) offer features or performance benefits that surpass their commercial counterparts in the core product development. Participation in OSS tools communities greatly dismantled the closed innovation model and lured organizations towards Open Innovation (OI). Harnessing the external knowledge that OI offers, requires better understanding regarding what to develop internally and what to acquire from outside the organization, how to cooperate with potential competitors, and when to conceal or reveal code while working with OSS communities. The aim of this thesis is to investigate how software-intensive organizations utilize the external and internal knowledge from OSS tools communities using Open Innovation to improve their core product development. First, this aim was achieved by exploring and reporting the existing evidence of OI in software engineering. Second, by providing a solution for software-intensive organizations regarding how to choose the right level of openness while working with OSS tools communities. Finally, we validated the proposed solution in multiple organizations. The thesis followed an empirical approach by conducting a systematic mapping study, case study, design science based contribution acceptance model, theory creation and validation of the theory. First, we conducted a systematic mapping study to synthesize the existing evidence on OI in software engineering and identified the research gaps. Second, we conducted an exploratory case study at Sony Mobile to explore how a software organization uses OSS tools communities to facilitate its core product development. Third, we proposed a theory of openness for organizations which provides guidelines regarding how to work with OSS tools communities. Fourth, we presented a contribution acceptance model and metamodel to assist strategic product planning in what to develop internally and what to share as OSS in the proprietary products.. Finally, we validated the proposed theory of openness for tools in two automotive companies by conducting focusgroups

Systematic Literature Review and Controlled Pilot Experimental Evaluation of Test Driven Development (TDD) vs. Test-Last Development (TLD)

Context: Test-Driven development (TDD) is a software development approach where test cases are written before actual development of the code in iterative cycles. TDD has gained attention of many software practitioners during the last decade since it has suggested several benefits in the software development process. However, empirical evidence of its dominance in terms of internal code quality, external code quality and productivity is fairly limited. Objectives: The aim behind conducting this study is to explore what has been achieved so far in the field of Test-driven development. The study reports the benefits and limitation of TDD compared to TLD and the outcome variables in all the reported studies along with their measurement criteria. Additionally, an experiment is conducted to see the impact of Test-driven development (TDD) on internal code quality, external code quality and productivity compared to Test-Last development (TLD). Methods: In this study two research methodologies are used specifically systematic literature review according to Kitchenham guidelines and controlled pilot experiment. In systematic literature review number of article sources are considered and used, including Inspec, Compendex, ACM, IEEE Xplore, Science direct (Elsevier) and ISI web of science. A review protocol is created first to ensure the objectivity and repeatability of the whole process. Second, a controlled experiment is conducted with professional software developers to explore the assumed benefits of Test-Driven development (TDD) compared to Test-Last development (TLD). Results: 9 distinct categories related to Test-driven development (TDD) are found that are investigated and reported in the literature. All the reported experiments revealing very little or no difference in internal code quality, external code quality and productivity in Test-Driven development (TDD) over Test-Last development (TLD). However, results were found contradictory when research methods are taken into account because case studies tend to find more positive results in the favor Test-Driven development (TDD) compared to experiments possibly due to the fact that experiment are mostly conducted in artificially created software development environment and mostly with students as a test subjects. On the other hand, experimental results and statistical analysis show no statistically significant result in the favor TDD compared to TLD. All the values found related to number of acceptance test cases passed (Mann-Whitney U test Exact Sig. 0.185), McCabe’s Cyclomatic complexity (Mann-Whitney U test Exact Sig. 0.063), Branch coverage (Mann-Whitney U test Exact Sig. 0.212), Productivity in terms of number of lines of code per person hours (Independent sample Ttest Sig. 0.686), productivity in terms number of user stories implemented per person hours (Independent sample T-test Sig. 0.835) in experiment are statistically insignificant. However, static code analysis (Independent sample T-test Sig. 0.03) result was found statistically significant but due to the low statistical power of test it was not possible to reject the null hypothesis. The results of the survey revealed that the majority of developers in the experiment prefer TLD over TDD, given the lesser required level of learning curve as well as the minimum effort needed to understand and employ TLD compared to TDD Conclusion: Systematic literature review confirms that the reported benefits of TDD development compared to Test-Last development are very small. However, case studies tend to find more positive results in the favor of Test-Driven development (TDD) compared to Test-Last development (TLD). Similarly, experimental findings are also confirming the fact that TDD has small benefits over TLD. However, given the small effect size there is an indication that (Test-Driven development) TDD endorses less complex code compared to Test-Last development (TLD).Systematic literature review confirms that the reported benefits of TDD development compared to Test-Last development are very small. However, case studies tend to find more positive results in the favor of Test-Driven development (TDD) compared to Test-Last development (TLD). Similarly, experimental findings are also confirming the fact that TDD has small benefits over TLD. However, given the small effect size there is an indication that (Test-Driven development) TDD endorses less complex code compared to Test-Last development (TLD)[email protected], [email protected]

Software testing in open innovation : An exploratory case study of the acceptance test harness for Jenkins

Open Innovation (OI) has gained significant attention since the term was introduced in 2003. However, little is known whether general software testing processes are well suited for OI. An exploratory case study on the Acceptance Test Harness (ATH) is conducted to investigate OI testing activities of Jenkins. As far as the research methodology is concerned, we extracted the change log data of ATH followed by five interviews with key contributors in the development of ATH. The findings of the study are threefold. First, it highlights the key stakeholders involved in the development of ATH. Second, the study compares the ATH testing activities with ISO/IEC/IEEE testing process and presents a tailored process for software testing in OI. Finally, the study underlines some key challenges that software intensive organizations face while working with the testing in OI

Systematic Literature Review and Controlled Pilot Experimental Evaluation of Test Driven Development (TDD) vs. Test-Last Development (TLD)

Context: Test-Driven development (TDD) is a software development approach where test cases are written before actual development of the code in iterative cycles. TDD has gained attention of many software practitioners during the last decade since it has suggested several benefits in the software development process. However, empirical evidence of its dominance in terms of internal code quality, external code quality and productivity is fairly limited. Objectives: The aim behind conducting this study is to explore what has been achieved so far in the field of Test-driven development. The study reports the benefits and limitation of TDD compared to TLD and the outcome variables in all the reported studies along with their measurement criteria. Additionally, an experiment is conducted to see the impact of Test-driven development (TDD) on internal code quality, external code quality and productivity compared to Test-Last development (TLD). Methods: In this study two research methodologies are used specifically systematic literature review according to Kitchenham guidelines and controlled pilot experiment. In systematic literature review number of article sources are considered and used, including Inspec, Compendex, ACM, IEEE Xplore, Science direct (Elsevier) and ISI web of science. A review protocol is created first to ensure the objectivity and repeatability of the whole process. Second, a controlled experiment is conducted with professional software developers to explore the assumed benefits of Test-Driven development (TDD) compared to Test-Last development (TLD). Results: 9 distinct categories related to Test-driven development (TDD) are found that are investigated and reported in the literature. All the reported experiments revealing very little or no difference in internal code quality, external code quality and productivity in Test-Driven development (TDD) over Test-Last development (TLD). However, results were found contradictory when research methods are taken into account because case studies tend to find more positive results in the favor Test-Driven development (TDD) compared to experiments possibly due to the fact that experiment are mostly conducted in artificially created software development environment and mostly with students as a test subjects. On the other hand, experimental results and statistical analysis show no statistically significant result in the favor TDD compared to TLD. All the values found related to number of acceptance test cases passed (Mann-Whitney U test Exact Sig. 0.185), McCabe’s Cyclomatic complexity (Mann-Whitney U test Exact Sig. 0.063), Branch coverage (Mann-Whitney U test Exact Sig. 0.212), Productivity in terms of number of lines of code per person hours (Independent sample Ttest Sig. 0.686), productivity in terms number of user stories implemented per person hours (Independent sample T-test Sig. 0.835) in experiment are statistically insignificant. However, static code analysis (Independent sample T-test Sig. 0.03) result was found statistically significant but due to the low statistical power of test it was not possible to reject the null hypothesis. The results of the survey revealed that the majority of developers in the experiment prefer TLD over TDD, given the lesser required level of learning curve as well as the minimum effort needed to understand and employ TLD compared to TDD Conclusion: Systematic literature review confirms that the reported benefits of TDD development compared to Test-Last development are very small. However, case studies tend to find more positive results in the favor of Test-Driven development (TDD) compared to Test-Last development (TLD). Similarly, experimental findings are also confirming the fact that TDD has small benefits over TLD. However, given the small effect size there is an indication that (Test-Driven development) TDD endorses less complex code compared to Test-Last development (TLD).Systematic literature review confirms that the reported benefits of TDD development compared to Test-Last development are very small. However, case studies tend to find more positive results in the favor of Test-Driven development (TDD) compared to Test-Last development (TLD). Similarly, experimental findings are also confirming the fact that TDD has small benefits over TLD. However, given the small effect size there is an indication that (Test-Driven development) TDD endorses less complex code compared to Test-Last development (TLD)[email protected], [email protected]

Considering rigor and relevance when evaluating test driven development: A systematic review

Context: Test driven development (TDD) has been extensively researched and compared to traditional approaches (test last development, TLD). Existing literature reviews show varying results for TDD. Objective: This study investigates how the conclusions of existing literature reviews change when taking two study quality dimension into account, namely rigor and relevance. Method: In this study a systematic literature review has been conducted and the results of the identified primary studies have been analyzed with respect to rigor and relevance scores using the assessment rubric proposed by Ivarsson and Gorschek 2011. Rigor and relevance are rated on a scale, which is explained in this paper. Four categories of studies were defined based on high/low rigor and relevance. Results: We found that studies in the four categories come to different conclusions. In particular, studies with a high rigor and relevance scores show clear results for improvement in external quality, which seem to come with a loss of productivity. At the same time high rigor and relevance studies only investigate a small set of variables. Other categories contain many studies showing no difference, hence biasing the results negatively for the overall set of primary studies. Given the classification differences to previous literature reviews could be highlighted. Conclusion: Strong indications are obtained that external quality is positively influenced, which has to be further substantiated by industry experiments and longitudinal case studies. Future studies in the high rigor and relevance category would contribute largely by focusing on a wider set of outcome variables (e.g. internal code quality). We also conclude that considering rigor and relevance in TDD evaluation is important given the differences in results between categories and in comparison to previous reviews. (C) 2014 Elsevier B.V. All rights reserved