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Background. The software architecture recovery method RELAX produces a concern-based architectural view of a software system graphically and textually from that system's source code. The method has been implemented in software which can be run on subject systems whose source code is written in Java. Aims. Our aim was to find out whether the availability of architectural views produced by RELAX can help maintainers who are new to a project in becoming productive with development tasks sooner, and find out how they felt about working in such an environment. Method. We conducted a user study with nine participants. They were subjected to a controlled experiment in which maintenance success and speed with and without access to RELAX recovery results were compared to each other. Results. We have observed that employing architecture views produced by RELAX helped participants reduce time to get started on maintenance tasks by a factor of 5.38 or more. While most participants were unable to finish their tasks within the allotted time when they did not have recovery results available, all of them finished them successfully when they did. Additionally, participants reported that these views were easy to understand, helped them to learn the system's structure and enabled them to compare different versions of the system. Conclusions. In the speedup experienced to the start of maintenance experienced by the participants as well as in their experience-based opinions, RELAX has shown itself to be a valuable help that could form the basis for further tools that specifically support the development process with a focus on maintenance., Comment: To be published in ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (ESEM '21)

Consumer product reviews are an invaluable source of data because they contain a wide range of information that could help requirement engineers to meet user needs. Recent studies have shown that tweets about software applications and reviews on App Stores contain useful information, which enable a more responsive software requirements elicitation. However, all of these studies' subjects are merely software applications. Information on system software, such as embedded software, operating systems, and firmware, are overlooked, unless reviews of a product using them are investigated. Challenges in investigating these reviews could come from the fact that there is a huge volume of data available, as well as the fact that reviews of such products are diverse in nature, meaning that they may contain information mostly on hardware components or broadly on the product as a whole. Motivated by these observations, we conduct an exploratory study using a dataset of 7198 review sentences from 6 Internet of Things (IoT) products. Our qualitative analysis demonstrates that a sufficient quantity of software related information exists in these reviews. In addition, we investigate the performance of two supervised machine learning techniques (Support Vector Machines and Convolutional Neural Networks) for classification of information contained in the reviews. Our results suggest that, with a certain setup, these two techniques can be used to classify the information automatically with high precision and recall., Comment: 14 pages, accepted to the Proceedings of CIbSE XXI Ibero-American Conference on Software Engineering (CIbSE), 2018 (ISBN: 9781510869370)