Browsing by Author "Fernández, Blanco Alison"

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    A manual categorization of new quality issues on automatically-generated tests
    (2023) Galindo-Gutierrez, Geraldine; Narea Carvajal, Maximiliano Agustín; Fernández, Blanco Alison
    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.
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    Asking and Answering Questions During Memory Profiling
    (2024) Fernández, Blanco Alison; Queriolo Córdova, Araceli; Juan Pablo, Sandoval Alcocer
    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.
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    DGT-AR: Visualizing Code Dependencies in AR
    (2023) Freire-Pozo, Dussan; Céspedes Arancibia, Kevin; Merino Del Campo, Leonel Alejandro; Fernández, Blanco Alison; Neyem, Hugo Andrés; Sandoval Alcocer, Juan Pablo
    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.
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    Software Visualizations to Analyze Memory Consumption: ALiterature Review
    (2022) Fernández, Blanco Alison; Bergel, Alexandre; Sandoval Alcocer, Juan Pablo
    Understanding and optimizing memory usage of software applications is a difficult task, usually involving the analysis of large amounts of memory-related complex data. Over the years, numerous software visualizations have been proposed to help developers analyze the memory usage information of their programs. This article reports a systematic literature review of published works centered on software visualizations for analyzing the memory consumption of programs. We have systematically selected 46 articles and categorized them based on the tasks supported, data collected, visualization techniques, evaluations conducted, and prototype availability. As a result, we introduce a taxonomy based on these five dimensions to identify the main challenges of visualizing memory consumption and opportunities for improvement. Despite the effort to evaluate visualizations, we also find that most articles lack evidence regarding how these visualizations perform in practice. We also highlight that few articles are available for developers willing to adopt a visualization for memory consumption analysis. Additionally, we describe a number of research areas that are worth exploring.
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    Visualizing Memory Consumption with Vismep
    (2022) Fernández, Blanco Alison; Bergel, Alexandre; Sandoval Alcocer, Juan Pablo; Queirolo Córdova, Araceli
    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.