Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SENTISTRENGTH and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and STACK OVERFLOW questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used.

Abstract-Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain.In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and STACK OVERFLOW questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to contradictory conclusions.

Model-based development and continuous integration each separately are methods to improve the productivity of development of complex modern software systems. We investigate industrial adoption of these two phenomena in combination, i.e., applying continuous integration practices in model-based development projects. Through semi-structured interviews, eleven engineers at three companies with different modelling practices share their views on perceived and experienced impediments to this adoption. We find some cases in which this introduction is undesired and expected to not be beneficial. For other cases, we find and categorize several impediments and discuss how they are dealt with in industrial practice. Model synchronization and tool interoperability are found the most challenging to overcome and the ways in which they are circumvented in practice are detrimental for introducing continuous integration.

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In model-based development projects, models at different abstraction levels capture different aspects of a software system, e.g., specification or design. Inconsistencies between these models can cause inefficient and incorrect development. A tool-based framework to assist developers creating and maintaining models conforming to different languages (i.e. heterogeneous models) and consistency between them is not only important but also much needed in practice. In this work, we focus on assisting developers bringing about multi-view consistency in the context of agile model-based development, through frequent, lightweight consistency checks across views and between heterogeneous models. The checks are lightweight in the sense that they are easy to create, edit, use and maintain, and since they find inconsistencies but do not attempt to automatically resolve them. With respect to ease of use, we explicitly separate the two main concerns in defining consistency checks, being (i) which modelling elements across heterogeneous models should be consistent with each other and (ii) what constitutes consistency between them. We assess the feasibility and illustrate the potential usefulness of our consistency checking approach, from an industrial agile model-based development point-of-view, through a proof-of-concept implementation on a sample project leveraging models expressed in SysML and Simulink. A continuous integration pipeline hosts the initial definition and subsequent execution of consistency checks, it is also the place where the user can view results of consistency checks and reconfigure them.

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This article presents the state of practice of consistency management in thirteen industrial model-based development settings. Our analysis shows a tight coupling between adopting shorter development cycles and increasingly pressing consistency management challenges. We find that practitioners desire to adopt shorter development cycles, but immature modeling practices slow them down. We describe the different patterns that emerge from the various industrial settings. There is an opportunity for researchers to provide practitioners with a migration path toward practices that enable more automated consistency management, and ultimately, continuous model-based development.

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