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Stakeholders in software projects use issue trackers like JIRA or Bugzilla to capture and manage issues, including requirements, feature requests, and bugs. To ease issue navigation and structure project knowledge, stakeholders manually connect issues via links of certain types that reflect different dependencies, such as Epic-, Block-, Duplicate-, or Relate- links. Based on a large dataset of 16 JIRA repositories, we study the commonalities and differences in linking practices and link types across the repositories. We then investigate how state-of-the-art machine learning models can predict common link types. We observed significant differences across the repositories and link types, depending on how they are used and by whom. Additionally, we observed several inconsistencies, e.g., in how Duplicate links are used. We found that a transformer model trained on titles and descriptions of linked issues significantly outperforms other optimized models, achieving an encouraging average macro F1-score of 0.64 for predicting nine popular link types across all repositories (weighted F1-score of 0.73). For the specific Subtask- and Epic- links, the model achieves top F1-scores of 0.89 and 0.97, respectively. If we restrict the task to predict the mere existence of links, the average macro F1-score goes up to 0.95. In general, the shorter issue text, possibly indicating precise issues, seems to improve the prediction accuracy with a strong negative correlation of - 0.73. We found that Relate-links often get confused with the other links, which suggests that they are likely used as default links in unclear cases. Our findings particularly on the quality and heterogeinity of issue link data have implications for researching and applying issue link prediction in practice. [ABSTRACT FROM AUTHOR]

Research has repeatedly shown that high-quality requirements are essential for the success of development projects. While the term "quality" is pervasive in the field of requirements engineering and while the body of research on requirements quality is large, there is no meta-study of the field that overviews and compares the concrete quality attributes addressed by the community. To fill this knowledge gap, we conducted a systematic mapping study of the scientific literature. We retrieved 6905 articles from six academic databases, which we filtered down to 105 relevant primary studies. The primary studies use empirical research to explicitly define, improve, or evaluate requirements quality. We found that empirical research on requirements quality focuses on improvement techniques, with very few primary studies addressing evidence-based definitions and evaluations of quality attributes. Among the 12 quality attributes identified, the most prominent in the field are ambiguity, completeness, consistency, and correctness. We identified 111 sub-types of quality attributes such as "template conformance" for consistency or "passive voice" for ambiguity. Ambiguity has the largest share of these sub-types. The artefacts being studied are mostly referred to in the broadest sense as "requirements", while little research targets quality attributes in specific types of requirements such as use cases or user stories. Our findings highlight the need to conduct more empirically grounded research defining requirements quality, using more varied research methods, and addressing a more diverse set of requirements types. [ABSTRACT FROM AUTHOR]

App stores include an increasing amount of user feedback in form of app ratings and reviews. Research and recently also tool vendors have proposed analytics and data mining solutions to leverage this feedback to developers and analysts, e.g., for supporting release decisions. Research also showed that positive feedback improves apps' downloads and sales figures and thus their success. As a side effect, a market for fake, incentivized app reviews emerged with yet unclear consequences for developers, app users, and app store operators. This paper studies fake reviews, their providers, characteristics, and how well they can be automatically detected. We conducted disguised questionnaires with 43 fake review providers and studied their review policies to understand their strategies and offers. By comparing 60,000 fake reviews with 62 million reviews from the Apple App Store we found significant differences, e.g., between the corresponding apps, reviewers, rating distribution, and frequency. This inspired the development of a simple classifier to automatically detect fake reviews in app stores. On a labelled and imbalanced dataset including one-tenth of fake reviews, as reported in other domains, our classifier achieved a recall of 91% and an AUC/ROC value of 98%. We discuss our findings and their impact on software engineering, app users, and app store operators. [ABSTRACT FROM AUTHOR]

Rationale refers to the reasoning and justification behind human decisions, opinions, and beliefs. In software engineering, rationale management focuses on capturing design and requirements decisions and on organizing and reusing project knowledge. This paper takes a different view on rationale written by users in online reviews. We studied 32,414 reviews for 52 software applications in the Amazon Store. Through a grounded theory approach and peer content analysis, we investigated how users argue and justify their decisions, e.g., about upgrading, installing, or switching software applications. We also studied the occurrence frequency of rationale concepts such as issues encountered or alternatives considered in the reviews and found that assessment criteria like performance, compatibility, and usability represent the most pervasive concept. We identified a moderate positive correlation between issues and criteria and furthermore assessed the distribution of rationale concepts with respect to rating and verbosity. We found that issues tend to appear more in lower star rated reviews, while criteria, alternatives, and justifications seem to appear more in three star rated reviews. Also, reviews reporting alternatives seem to be more verbose than reviews reporting criteria. A follow-up qualitative study of sub-concepts revealed, that users also report other alternatives (e.g., alternative software provider), criteria (e.g., cost), and decisions (e.g., on rating software). We then used the truth set of manually labeled review sentences to explore how accurately we can mine rationale concepts from the reviews. We evaluated the classification algorithms Naive Bayes, Support Vector Machine, Logistic Regression, Decision Tree, Gaussian Process, Random Forest, and Multilayer Perceptron Classifier using a baseline and random configuration. Support Vector Classifier, Naive Bayes, and Logistic Regression, trained on the review metadata, syntax tree of the review text, and influential terms, achieved a precision around 80% for predicting sentences with alternatives and decisions, with top recall values of 98%. On the review level, precision was up to 13% higher with recall values reaching 99%. Using only word features, we achieved in most cases the highest precision and highest recall respectively using the Random Forest and Naive Bayes algorithm. We discuss the findings and the rationale importance for supporting deliberation in user communities and synthesizing the reviews for developers. [ABSTRACT FROM AUTHOR]

App stores like Google Play and Apple AppStore have over 3 million apps covering nearly every kind of software and service. Billions of users regularly download, use, and review these apps. Recent studies have shown that reviews written by the users represent a rich source of information for the app vendors and the developers, as they include information about bugs, ideas for new features, or documentation of released features. The majority of the reviews, however, is rather non-informative just praising the app and repeating to the star ratings in words. This paper introduces several probabilistic techniques to classify app reviews into four types: bug reports, feature requests, user experiences, and text ratings. For this, we use review metadata such as the star rating and the tense, as well as, text classification, natural language processing, and sentiment analysis techniques. We conducted a series of experiments to compare the accuracy of the techniques and compared them with simple string matching. We found that metadata alone results in a poor classification accuracy. When combined with simple text classification and natural language preprocessing of the text-particularly with bigrams and lemmatization-the classification precision for all review types got up to 88-92 % and the recall up to 90-99 %. Multiple binary classifiers outperformed single multiclass classifiers. Our results inspired the design of a review analytics tool, which should help app vendors and developers deal with the large amount of reviews, filter critical reviews, and assign them to the appropriate stakeholders. We describe the tool main features and summarize nine interviews with practitioners on how review analytics tools including ours could be used in practice. [ABSTRACT FROM AUTHOR]

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The original article can be found online at https://doi.org/10.1007/s00766-021-00367-z. Correction to: Requirements Engineering https://doi.org/10.1007/s00766-021-00367-z In this article the caption to Figure 6 and Figure 7 was inadvertently swapped. [Extracted from the article]

We report on an exploratory study, which aims at understanding how software communities use blogs compared to conventional development infrastructures. We analyzed the behavior of 1,100 bloggers in four large open source communities, distinguishing between committing bloggers and other community members. We observed that these communities intensively use blogs with one new entry every 8 h. A blog entry includes 14 times more words than a commit message. When analyzing the content of the blogs, we found that committers and others bloggers write about similar topics. Most popular topics in committers’ blogs represent high-level concepts such as features and domain concepts, while source code related topics are discussed in 15% of their posts. Other community members frequently write about community events and conferences as well as configuration and deployment topics. We found that the blogging peak period is usually after the software is released. Moreover, committers are more likely to blog after corrective engineering than after forward engineering and re-engineering activities. Our findings call for a hypothesis-driven research to (a) further understand the role of social media in dissolving the collaboration boundaries between developers and other stakeholders and (b) integrate social media into development processes and tools. [ABSTRACT FROM AUTHOR]

The I IEEE International Conference on Requirements Engineering i (RE) is the premier conference on all aspects of requirements-related research. The industry papers at RE'18 were renamed Industry Innovation papers to stress the nature and importance of these contributions to the RE community. The four research papers with the best review scores and explicit support of the PC, together with the best industry innovation paper, were invited to submit an extended version to this special issue of REJ. [Extracted from the article]