Context Secondary studies are vulnerable to threats to validity. Although, mitigating these threats is crucial for the credibility of these studies, we currently lack a systematic approach to identify, categorize and mitigate threats to validity for secondary studies. Objective In this paper, we review the corpus of secondary studies, with the aim to identify: (a) the trend of reporting threats to validity, (b) the most common threats to validity and corresponding mitigation actions, and (c) possible categories in which threats to validity can be classified. Method To achieve this goal we employ the tertiary study research method that is used for synthesizing knowledge from existing secondary studies. In particular, we collected data from more than 100 studies, published until December 2016 in top quality software engineering venues (both journals and conference). Results Our results suggest that in recent years, secondary studies are more likely to report their threats to validity. However, the presentation of such threats is rather ad hoc, e.g., the same threat may be presented with a different name, or under a different category. To alleviate this problem, we propose a classification schema for reporting threats to validity and possible mitigation actions. Both the classification of threats and the associated mitigation actions have been validated by an empirical study, i.e., Delphi rounds with experts. Conclusion Based on the proposed schema, we provide a checklist, which authors of secondary studies can use for identifying and categorizing threats to validity and corresponding mitigation actions, while readers of secondary studies can use the checklist for assessing the validity of the reported results.

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Identifying, categorizing and mitigating threats to validity in software engineering secondary studies

UML4IoT-A UML-based approach to exploit IoT in cyber-physical manufacturing systems

How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model

Industry 4.0 integrates cyber-physical systems with the Internet of Things to optimize the complete value-added chain. Successfully applying Industry 4.0 requires the cooperation of various stakeholders from different domains. Domain-specific modeling languages promise to facilitate their involvement through leveraging (domain-specific) models to primary development artifacts. We aim to assess the use of modeling in Industry 4.0 through the lens of modeling languages in a broad sense. Based on an extensive literature review, we updated our systematic mapping study on modeling languages and modeling techniques used in Industry 4.0  (Wortmann et al., Conference on model-driven engineering languages and systems (MODELS’17), IEEE, pp 281–291, 2017) to include publications until February 2018. Overall, the updated study considers 3344 candidate publications that were systematically investigated until 408 relevant publications were identified. Based on these, we developed an updated map of the research landscape on modeling languages and techniques for Industry 4.0. Research on modeling languages in Industry 4.0 focuses on contributing methods to solve the challenges of digital representation and integration. To this end, languages from systems engineering and knowledge representation are applied most often but rarely combined. There also is a gap between the communities researching and applying modeling languages for Industry 4.0 that originates from different perspectives on modeling and related standards. From the vantage point of modeling, Industry 4.0 is the combination of systems engineering, with cyber-physical systems, and knowledge engineering. Research currently is splintered along topics and communities and accelerating progress demands for multi-disciplinary, integrated research efforts.

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Modeling languages in Industry 4.0: an extended systematic mapping study

Planning and managing requirements changes in Global Software Development (GSD) is a challenging task. While requirements change has received much attention from researchers, Requirements Change Management (RCM) process is still an emerging area in GSD. The objective of this paper is to identify the success factors of RCM in the GSD environment. We applied the Systematic Literature Review (SLR) approach and identified 23 success factors that influence RCM in GSD projects. The findings of the SLR indicate that change impact analysis, change understanding, management support, RCM process awareness, standard for RCM, progress measure, updated requirements and minimize project failure risk are key factors that influence RCM in a GSD project. We present a comparison of success factors identified in client and vendor organizations. Moreover, we present a framework to classify the identified success factors for RCM process implementation. We believe GSD organizations can use the framework to better manage requirements change in GSD projects.

Success factors influencing requirements change management process in global software development

Context: In this study we report on a Systematic Mapping Study (SMS) for Domain-Specific Languages (DSLs), based on an automatic search including primary studies from journals, conferences, and workshops during the period from 2006 until 2012.Objective: The main objective of the described work was to perform an SMS on DSLs to better understand the DSL research field, identify research trends, and any possible open issues. The set of research questions was inspired by a DSL survey paper published in 2005.Method: We conducted a SMS over 5 stages: defining research questions, conducting the search, screening, classifying, and data extraction. Our SMS included 1153 candidate primary studies from the ISI Web of Science and ACM Digital Library, 390 primary studies were classified after screening.Results: This SMS discusses two main research questions: research space and trends/demographics of the literature within the field of DSLs. Both research questions are further subdivided into several research sub-questions. The results from the first research question clearly show that the DSL community focuses more on the development of new techniques/methods rather than investigating the integrations of DSLs with other software engineering processes or measuring the effectiveness of DSL approaches. Furthermore, there is a clear lack of evaluation research. Amongst different DSL development phases more attention is needed in regard to domain analysis, validation, and maintenance. The second research question revealed that the number of publications remains stable, and has not increased over the years. Top cited papers and venues are mentioned, as well as identifying the more active institutions carrying DSL research.Conclusion: The statistical findings regarding research questions paint an interesting picture about the mainstreams of the DSL community, as well as open issues where researchers can improve their research in their future work.

Sudev Bohra

Papers

Domain-Specific Languages

A Systematic Mapping Study driven by the margin of error