Abstract Code reviewing is a widespread practice used by software engineers to maintain high code quality. To date, the knowledge on the effect of code review on source code is still limited. Some studies have addressed this problem by classifying the types of changes that take place during the review process (a.k.a. review changes), as this strategy can, for example, pinpoint the immediate effect of reviews on code. Nevertheless, this classification (1) is not scalable, as it was conducted manually, and (2) was not assessed in terms of how meaningful the provided information is for practitioners. This paper aims at addressing these limitations: First, we investigate to what extent a machine learning-based technique can automatically classify review changes. Then, we evaluate the relevance of information on review change types and its potential usefulness, by conducting (1) semi-structured interviews with 12 developers and (2) a qualitative study with 17 developers, who are asked to assess reports on the review changes of their project. Key results of the study show that not only it is possible to automatically classify code review changes, but this information is also perceived by practitioners as valuable to improve the code review process. Data and materials: 10.5281/zenodo.5592254

A Dark Pattern (DP) is an interface maliciously crafted to deceive users into performing actions they did not mean to do. In this work, we analyze Dark Patterns in 240 popular mobile apps and conduct an online experiment with 589 users on how they perceive Dark Patterns in such apps. The results of the analysis show that 95% of the analyzed apps contain one or more forms of Dark Patterns and, on average, popular applications include at least seven different types of deceiving interfaces. The online experiment shows that most users do not recognize Dark Patterns, but can perform better in recognizing malicious designs if informed on the issue. We discuss the impact of our work and what measures could be applied to alleviate the issue.

Software developers typically use multiple large screens in their office setup. However, they often work away from the office where such a setup is not available, instead only working with a laptop computer and drastically reduced screen real estate. We explore how developers can be better supported in ad-hoc scenarios, for example when they work in a cafe, an airport, or at a client's site. We present insights into current work practices and challenges when working away from the usual office desk sourced from a survey of professional software developers. Based on these insights, we introduce an IDE that makes use of additional personal devices, such as a phone or a tablet. Parts of the IDE can be offloaded to these mobile devices, for example the application that is being developed, a debugging console or navigational elements. A qualitative evaluation with professional software developers showed that they appreciate the increased screen real estate.

The number of smart devices that people own or share with family and friends has increased dramatically. As a result, users often want to copy data among devices such as smart-phones, tablets and desktop computers. While various chat and cloud services support the sharing of data, they require users to interrupt their workflow to copy resources. We present MyoShare, a system that allows content to be shared among devices using mid-air gestures that can be used at any time, independent of the current task and location of devices. We report on an elicitation study where participants designed a set of gestures for sharing content. In a second user study, we compared mid-air gestures with alternative interaction modes using keyboard or touch Shortcuts, Speech, and Menu Selection. We discuss the results of the study in terms of both the strengths and weaknesses of mid-air gestures, along with suggestions for future work.

With the growth of mobile devices, the necessity for a more ubiquitous network among the smartphones, tablets and computers of users increases. Sending and receiving content among personal devices should be an intuitive rather than a distracting task to be performed. With MyoShare, we propose a system that exploits the use of mid-air gestures, captured via the Myo wearable armband, to share web data among devices without requiring the user to open additional windows or copy-paste content into emails or chat applications. Users can select content from any web page and send it to another device by simply waving their hands. During the demo session, participants of the conference will be able to try out and see our system in action.

Tilt-and-Tap (TAT) is a jQuery-like framework that supports the rapid development of web applications that use tilting gestures for interaction. Such gestures enable users to interact with, and navigate through, websites by simply moving their mobile device in combination with screen taps. Building on this, we have also developed a second framework Cross-Tilt-and-Tap (CTAT) that supports the rapid development of applications that use motion-basd gestures in a range of multi-device scenarios. Our demo not only shows a range of applications that we have developed using the frameworks, but also the frameworks in action by demonstrating the steps involved in developing and adapting applications.

Software testing represents one of the most explored fields of application of Search-Based techniques and a range of testing problems have been successfully addressed using Genetic Algorithms. Nevertheless, to date Search-Based Software Testing (SBST) has found limited application in industry. As in other fields of Search-Based Software Engineering, this is principally due to the fact that when applied to large problems, Search-Based approaches may require too much computational efforts. In this scenario, parallelization may be a suitable way to improve the performance especially due to the fact that many of these techniques are "naturally parallelizable". Nevertheless, very few attempts have been provided for SBST parallelization.In this paper, we present a Parallel Genetic Algorithm for the automatic generation of test suites. The solution is based on Hadoop MapReduce since it is well supported to work also in the cloud and on graphic cards, thus being an ideal candidate for high scalable parallelization of Genetic Algorithms. A preliminary analysis of the proposal was carried outaiming to evaluate the speed-up with respect to the sequential execution. The analysis was based on a real world open source library.