Mobile applications play a relevant role in users' daily lives by improving and easing daily processes such as commuting or making financial transactions. The aforementioned interactions enhance the usability of commonly used services. Nevertheless, the improvements should also consider special execution environments such as weak network connections or special requirements inherited from the user's condition. Due to this, the design of mobile applications should be driven by improving the user experience. This tutorial targets the usage of inclusive and accessibility design in the development process of mobile apps. Making sure that applications are accessible to all users, regardless of disabilities, is not just about following the law or fulfilling ethical obligations; it is crucial in creating inclusive and fair digital environments. This tutorial will educate participants on accessibility principles and the available tools. They will gain practical experience with specific Android and iOS platform features, as well as become acquainted with state-of - the-art automated and manual testing tools.

In software engineering pedagogy, a persistent challenge is the comprehensive assessment of student contributions within software repositories. This study delves into the investigation of the Git-Truck tool, initially designed for professional software engineers, and explores its adaptability and effectiveness within an academic setting. We specifically focus on the tool's potential for educators when assessing Capstone software repositories. Our results emphasize that educators found bubble chart visualization and metrics such as ''Top Contributor'' and ''Number of Commits'' helpful in understanding group dynamics and contribution. We also discuss the tool's limitations among visual techniques and metrics used. As the educational landscape shifts towards increased virtual and remote modalities, tools like Git-Truck are poised to augment the intricacy and depth of software project evaluations. For those considering adopting or adapting such tools in similar contexts, our study offers the challenges and lessons learned from this experience.

The software engineering community has produced numerous tools, techniques, and methodologies for practitioners to analyze and optimize memory usage during software execution. However, little is known about the actual needs of programmers when analyzing memory behavior and how they use tools to address those needs. We conducted an exploratory study (i) to understand what a programmer needs to know when analyzing memory behavior and (ii) how a programmer finds that information with current tools. From our observations, we provide a catalog of 34 questions programmers ask themselves when analyzing memory behavior. We also report a detailed analysis of how some tools are used to answer these questions and the difficulties participants face during the process. Finally, we present four recommendations to guide researchers and developers in designing, evaluating, and improving memory behavior analysis tools.

Diverse studies have analyzed the quality of automatically generated test cases by using test smells as the main quality attribute. But recent work reported that generated tests might suffer from a number of quality issues not considered previously, thus suggesting that not all test smells have been identified yet. Little is known about these issues and their frequency within generated tests. In this paper, we report on a manual analysis of an external dataset consisting of 2,340 automatically generated tests. This analysis aimed at detecting new quality issues, not covered by past recognized test smells. We use thematic analysis to group and categorize the new quality issues found. As a result, we propose a taxonomy of 13 new quality issues grouped in four categories. We also report on the frequency of these new quality issues within the dataset and present eight recommendations that test generators may consider to improve the quality and usefulness of the automatically generated tests. As an additional contribution, our results suggest that (i) test quality should be evaluated not only on the tests themselves, but considering also the tested code; and (ii) automatically generated tests present flaws that are unlikely to be found in manually created tests and thus require specific quality checking tools.

Analyzing source code dependencies between components within a program is an essential activity in software development. While various software visualization tools have been proposed to aid in this activity, most are limited to desktop applications. As a result, the potential impact of augmented reality (AR) on improving dependency analysis remains largely unexplored. In this paper, we present DGT-AR, a node-link visualization tool for code dependencies in immersive augmented reality. DG T-AR extends the physical screen space of IDEs to the infinite virtual space. That is, developers neither have to sacrifice screen space nor leave the IDE and use third-party applications. We present the preliminary results of a pilot user study along with four key lessons learned. Additionally, we have made DGT-AR publicly available.

Detecting and repairing memory issues is still a challenging task. One reason is that understanding a program's memory usage involves a diverse and related set of dynamic and static aspects. Over the years, multiple tools have been proposed to assist practitioners in these activities. However, detailed information about how a tool helps users when analyzing memory usage is missing.This article introduces Vismep, an interactive visualization prototype to help programmers analyze Python applications' memory usage, and presents an exploratory study to understand the behavior and perception of users when using Vismep. As a result, we reported five information needs when participants analyze memory consumption and how they use Vismep to satisfy these needs. Besides, participants positively perceived Vismep due to their valuable views and high overall usability.

Profiling the memory consumption of a software execution is usually carried out by characterizing calling-context trees. However, the plurality nature of this data-structure makes it difficult to adequately and efficiently exploit in practice. As a consequence, most of anomalies in memory footprints are addressed either manually or in an ad-hoc way. We propose an interactive visualization of the execution context related to object productions. Our visualization augments the traditional calling-context tree with visual cues to characterize object allocation sites.We performed a qualitative study involving eight software engineers conducting a software execution memory assessment. As a result, we found that participants find our visualization as beneficial to characterizing a memory consumption and to reducing the overall memory footprint.