Software has become ubiquitous in every corner of modern societies. During the last five decades, software engineering has also changed significantly to advance the development of various types and ...scales of software products. In this context, Software Engineering Education plays an important role in keeping students updated with software technologies, processes, and practices that are popular in industries.
We investigate from literature the extent Software Engineering Education addresses major Software Engineering Trends in the academic setting.
We conducted a systematic mapping study about teaching major Software Engineering Trends in project courses. We classified 126 papers based on their investigated Software Engineering Trends, specifically Software Engineering processes and practices, teaching approaches, and the evolution of Software Engineering Trends over time.
We reveal that Agile Software Development is the major trend. The other Trends, i.e., Software Implementation, Usability and Value, Global Software Engineering, and Lean Software Startup, are relatively small in the academic setting, but continuously growing in the last five years. System of Systems is the least investigated among all Trends.
The study points out the possible gaps between Software Industry and Education, which implies actionable insights for researchers, educators, and practitioners.
•Software Engineering Education is shifting to adapt to industrial movements.•Agile Software Development is a popular and increasing Software Engineering Trend.•GSE and Lean Software Startups are recently emerging Trends.•Software Engineering Trends are mainly addressed by Project-based learning.•Agile methodologies are combined in other SE trends, such as GSE and Lean Startup.
•A method for engineering gamified software is presented. It was developed via:1.Systematic review of past literature and methods to identify design principles2.Interviews with 25 leading ...gamification industry experts3.Evaluations in a practical use case and via industry expert interviews•Evaluations indicate the method is comprehensive, highly usable, and complete.
Since its inception around 2010, gamification has become one of the top technology and software trends. However, gamification has also been regarded as one of the most challenging areas of software engineering. Beyond traditional software design requirements, designing gamification requires the command of disciplines such as (motivational/behavioral) psychology, game design, and narratology, making the development of gamified software a challenge for traditional software developers. Gamification software inhabits a finely tuned niche of software engineering that seeks for both high functionality and engagement; beyond technical flawlessness, gamification has to motivate and affect users. Consequently, it has also been projected that most gamified software is doomed to fail.
This paper seeks to advance the understanding of designing gamification and to provide a comprehensive method for developing gamified software.
We approach the research problem via a design science research approach; firstly, by synthesizing the current body of literature on gamification design methods and by interviewing 25 gamification experts, producing a comprehensive list of design principles for developing gamified software. Secondly, and more importantly, we develop a detailed method for engineering of gamified software based on the gathered knowledge and design principles. Finally, we conduct an evaluation of the artifacts via interviews of ten gamification experts and implementation of the engineering method in a gamification project.
As results of the study, we present the method and key design principles for engineering gamified software. Based on the empirical and expert evaluation, the developed method was deemed as comprehensive, implementable, complete, and useful. We deliver a comprehensive overview of gamification guidelines and shed novel insights into the nature of gamification development and design discourse.
This paper takes first steps towards a comprehensive method for gamified software engineering.
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Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J comes with a test suite and at least one failing test case that triggers the bug. In this paper, ...we report on an experiment to explore the effectiveness of automatic test-suite based repair on Defects4J. The result of our experiment shows that the considered state-of-the-art repair methods can generate patches for 47 out of 224 bugs. However, those patches are only test-suite adequate, which means that they pass the test suite and may potentially be incorrect beyond the test-suite satisfaction correctness criterion. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly repaired with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial or incorrect patches still pass the test suite. With respect to practical applicability, it takes on average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All the repair systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair.
To perform their daily tasks, developers intensively make use of existing resources by consulting open source software (OSS) repositories. Such platforms contain rich data sources, e.g., code ...snippets, documentations, and user discussions, that can be useful for supporting development activities. Over the last decades, several techniques and tools have been promoted to provide developers with innovative features, aiming to bring in improvements in terms of development effort, cost savings, and productivity. In the context of the EU H2020 CROSSMINER project, a set of recommendation systems has been conceived to assist software programmers in different phases of the development process. The systems provide developers with various artifacts, such as third-party libraries, documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This paper is an experience report to present the knowledge pertinent to the set of recommendation systems developed through the CROSSMINER project. We explain in detail the challenges we had to deal with, together with the related lessons learned when developing and evaluating these systems. Our aim is to provide the research community with concrete takeaway messages that are expected to be useful for those who want to develop or customize their own recommendation systems. The reported experiences can facilitate interesting discussions and research work, which in the end contribute to the advancement of recommendation systems applied to solve different issues in Software Engineering.
This paper provides a comprehensive survey of techniques for testing machine learning systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., ...correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.
For software engineering research to increase its impact and steer our community toward a more successful future, it must foster context-driven research. Such research focuses on problems defined in ...collaboration with industrial partners and is driven by concrete needs in specific domains and development projects.
Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events ...such as workload changes and component failures. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.
Estimating and understanding software development productivity represent crucial tasks for researchers and practitioners. Although different works focused on evaluating the impact of human factors on ...productivity, a few explored the influence of cultural/geographical diversity in software development communities. More particularly, all previous treatise addresses cultural aspects as abstract concepts without providing a quantitative representation. Improved knowledge of these matters might help project managers to assemble more productive teams and tool vendors to design software analytics toolkits that may better estimate productivity. This paper has the goal of enlarging the existing body of knowledge on the factors affecting productivity by focusing on cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it. To reach this goal, we performed a mixed-method empirical study. First, we built a statistical model relating dispersion metrics with the productivity of 25 open-source communities on Github. Then, we performed a confirmatory survey with 140 practitioners. The key results of our study indicate that cultural and geographical dispersion considerably impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle. We conclude our paper by elaborating on the main insights from our analyses and instilling implications that may drive further research.
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•We use Hofstede dimensions to represent culture and cultural dispersion.•Cultural and geographical dispersion impact the productivity of a community.•Power Distance and Long Term Orientation Dispersion negatively impact productivity.•Individualism and Indulgence Dispersion positively impacts productivity.•Geographical Dispersion positively impacts productivity.
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