arXiv subjects

Márcio Ribeiro

Publications and source records attributed to Márcio Ribeiro.

29 records · Page 2Linked to original sources

arXiv2024

Evaluating Large Language Models in Detecting Test Smells

Test smells are coding issues that typically arise from inadequate practices, a lack of knowledge about effective testing, or deadline pressures to complete projects. The presence of test smells can negatively impact the maintainability and reliability of software. While there are tools that use advanced static analysis or machine learning techniques to detect test smells, these tools often require effort to be used. This study aims to evaluate the capability of Large Language Models (LLMs) in automatically detecting test smells. We evaluated ChatGPT-4, Mistral Large, and Gemini Advanced using 30 types of test smells across codebases in seven different programming languages collected from the literature. ChatGPT-4 identified 21 types of test smells. Gemini Advanced identified 17 types, while Mistral Large detected 15 types of test smells. Conclusion: The LLMs demonstrated potential as a valuable tool in identifying test smells.

cs.SE↗

arXiv2024

A Catalog of Transformations to Remove Smells From Natural Language Tests

Test smells can pose difficulties during testing activities, such as poor maintainability, non-deterministic behavior, and incomplete verification. Existing research has extensively addressed test smells in automated software tests but little attention has been given to smells in natural language tests. While some research has identified and catalogued such smells, there is a lack of systematic approaches for their removal. Consequently, there is also a lack of tools to automatically identify and remove natural language test smells. This paper introduces a catalog of transformations designed to remove seven natural language test smells and a companion tool implemented using Natural Language Processing (NLP) techniques. Our work aims to enhance the quality and reliability of natural language tests during software development. The research employs a two-fold empirical strategy to evaluate its contributions. First, a survey involving 15 software testing professionals assesses the acceptance and usefulness of the catalog's transformations. Second, an empirical study evaluates our tool to remove natural language test smells by analyzing a sample of real-practice tests from the Ubuntu OS. The results indicate that software testing professionals find the transformations valuable. Additionally, the automated tool demonstrates a good level of precision, as evidenced by a F-Measure rate of 83.70%

cs.SE↗

arXiv2023

Manual Tests Do Smell! Cataloging and Identifying Natural Language Test Smells

Background: Test smells indicate potential problems in the design and implementation of automated software tests that may negatively impact test code maintainability, coverage, and reliability. When poorly described, manual tests written in natural language may suffer from related problems, which enable their analysis from the point of view of test smells. Despite the possible prejudice to manually tested software products, little is known about test smells in manual tests, which results in many open questions regarding their types, frequency, and harm to tests written in natural language. Aims: Therefore, this study aims to contribute to a catalog of test smells for manual tests. Method: We perform a two-fold empirical strategy. First, an exploratory study in manual tests of three systems: the Ubuntu Operational System, the Brazilian Electronic Voting Machine, and the User Interface of a large smartphone manufacturer. We use our findings to propose a catalog of eight test smells and identification rules based on syntactical and morphological text analysis, validating our catalog with 24 in-company test engineers. Second, using our proposals, we create a tool based on Natural Language Processing (NLP) to analyze the subject systems' tests, validating the results. Results: We observed the occurrence of eight test smells. A survey of 24 in-company test professionals showed that 80.7% agreed with our catalog definitions and examples. Our NLP-based tool achieved a precision of 92%, recall of 95%, and f-measure of 93.5%, and its execution evidenced 13,169 occurrences of our cataloged test smells in the analyzed systems. Conclusion: We contribute with a catalog of natural language test smells and novel detection strategies that better explore the capabilities of current NLP mechanisms with promising results and reduced effort to analyze tests written in different idioms.

cs.SE↗

arXiv2021

Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification

The Android mining sandbox approach consists in running dynamic analysis tools on a benign version of an Android app and recording every call to sensitive APIs. Later, one can use this information to (a) prevent calls to other sensitive APIs (those not previously recorded) or (b) run the dynamic analysis tools again in a different version of the app -- in order to identify possible malicious behavior. Although the use of dynamic analysis for mining Android sandboxes has been empirically investigated before, little is known about the potential benefits of combining static analysis with the mining sandbox approach for identifying malicious behavior. As such, in this paper we present the results of two empirical studies: The first is a non-exact replication of a previous research work from Bao et al., which compares the performance of test case generation tools for mining Android sandboxes. The second is a new experiment to investigate the implications of using taint analysis algorithms to complement the mining sandbox approach in the task to identify malicious behavior. Our study brings several findings. For instance, the first study reveals that a static analysis component of DroidFax (a tool used for instrumenting Android apps in the Bao et al. study) contributes substantially to the performance of the dynamic analysis tools explored in the previous work. The results of the second study show that taint analysis is also practical to complement the mining sandboxes approach, improve the performance of the later strategy in at most 28.57%.

cs.CR↗

arXiv2021

What Evidence We Would Miss If We Do Not Use Grey Literature?

Context: Over the last years, Grey Literature (GL) is gaining increasing attention in Secondary Studies in Software Engineering (SE). Notably, Multivocal Literature Review (MLR) studies, that search for evidence in both Traditional Literature (TL) and GL, is particularly benefiting from this raise of GL content. Despite the growing interest in MLR-based studies, the literature assessing how GL has contributed to MLR studies is still scarce. Objective: This research aims to assess how the use of GL contributed to MLR studies. By contributing, we mean, understanding to what extent GL is providing evidence that is indeed used by an MLR to answer its research question. Method: We conducted a tertiary study to identify MLR studies published between 2017 and 2019, selecting nine MLRs studies. Using qualitative and quantitative analysis, we identified the GL used and assessed to what extent these MLRs are contributing to MLR studies. Results: Our analysis identified that 1) GL provided evidence not found in TL, 2) most of the GL sources were used to provide recommendations to solve problems, explain a topic, and classify the findings, and 3) 19 different GL types were used in the studies; these GLs were mainly produced by SE practitioners (including blog posts, slides presentations, or project descriptions). Conclusions: We evidence how GL contributed to MLR studies. We observed that if these GLs were not included in the MLR, several findings would have been omitted or weakened. We also described the challenges involved when conducting this investigation, along with potential ways to deal with them, which may help future SE researchers.

cs.SE↗

arXiv2021

Grey Literature in Software Engineering: A Critical Review

Context: Grey Literature (GL) recently has grown in Software Engineering (SE) research since the increased use of online communication channels by software engineers. However, there is still a limited understanding of how SE research is taking advantage of GL. Objective: This research aimed to understand how SE researchers use GL in their secondary studies. Method: We conducted a tertiary study of studies published between 2011 and 2018 in high-quality software engineering conferences and journals. We then applied qualitative and quantitative analysis to investigate 446 potential studies. Results: From the 446 selected studies, 126 studies cited GL but only 95 of those used GL to answer a specific research question representing almost 21% of all the 446 secondary studies. Interestingly, we identified that few studies employed specific search mechanisms and used additional criteria for assessing GL. Moreover, by the time we conducted this research, 49% of the GL URLs are not working anymore. Based on our findings, we discuss some challenges in using GL and potential mitigation plans. Conclusion: In this paper, we summarized the last 10 years of software engineering research that uses GL, showing that GL has been essential for bringing practical new perspectives that are scarce in traditional literature. By drawing the current landscape of use, we also raise some awareness of related challenges (and strategies to deal with them).

cs.SE↗

arXiv2020

On the Use of Grey Literature: A Survey with the Brazilian Software Engineering Research Community

Background: The use of Grey Literature (GL) has been investigate in diverse research areas. In Software Engineering (SE), this topic has an increasing interest over the last years. Problem: Even with the increase of GL published in diverse sources, the understanding of their use on the SE research community is still controversial. Objective: To understand how Brazilian SE researchers use GL, we aimed to become aware of the criteria to assess the credibility of their use, as well as the benefits and challenges. Method: We surveyed 76 active SE researchers participants of a flagship SE conference in Brazil, using a questionnaire with 11 questions to share their views on the use of GL in the context of SE research. We followed a qualitative approach to analyze open questions. Results: We found that most surveyed researchers use GL mainly to understand new topics. Our work identified new findings, including: 1) GL sources used by SE researchers (e.g., blogs, community website); 2) motivations to use (e.g., to understand problems and to complement research findings) or reasons to avoid GL (e.g., lack of reliability, lack of scientific value); 3) the benefit that is easy to access and read GL and the challenge of GL to have its scientific value recognized; and 4) criteria to assess GL credibility, showing the importance of the content owner to be renowned (e.g., renowned author and institutions). Conclusions: Our findings contribute to form a body of knowledge on the use of GL by SE researchers, by discussing novel (some contradictory) results and providing a set of lessons learned to both SE researchers and practitioners.

cs.SE↗

arXiv2020

Influence of Technical and Social Factors for Introducing Bugs

[This paper has been withdrawn by the author due to updated research available on arXiv (arXiv:1811.01918)] As the modern open-source paradigm makes it easier to contribute to software projects, the number of developers involved in these projects keep increasing. This growth in the amount of developers makes it more difficult to deal with harmful contributions. Recent researches have found that technical and social factors can predict the success of contributions to open-source projects on GitHub. However, these researches do not study the relation between these factors with the introduction of bugs. Our study aims at investigating the influence of technical (such as, developers' experience) and social (such as, number of followers) factors on the introduction of bugs, using information from 14 projects hosted on GitHub. Understanding the influence of these factors may be useful to developers, code reviewers and researchers. For instance, code reviewers may want to double check commits from developers that present bug-related factors. We found that technical factors have a consistent influence in the introduction of bugs. On the other hand, social factors present signs of influence in bug introduction that would require more data to be properly evaluated. Moreover, we found that perils present in the mining of GitHub may impact the factors results.

cs.SE↗

arXiv2020

On Relating Technical, Social Factors, and the Introduction of Bugs

As collaborative coding environments make it easier to contribute to software projects, the number of developers involved in these projects keeps increasing. This increase makes it more difficult for code reviewers to deal with buggy contributions. Collaborative environments like GitHub provide a rich source of data on developers' contributions. Such data can be used to extract information about developers regarding technical (e.g., their experience) and social (e.g., their interactions) factors. Recent studies analyzed the influence of these factors on different activities of software development. However, there is still room for improvement on the relation between these factors and the introduction of bugs. We present a broader study, including 8 projects from different domains and 6,537 bug reports, on relating five technical, three social factors, and the introduction of bugs. The results indicate that technical and social factors can discriminate between buggy and clean commits. But, the technical factors are more determining than social ones. Particularly, the developers' habits of not following technical contribution norms and the developer's commit bugginess are associated with an increase on commit bugginess. On the other hand, project's establishment, ownership level of developers' commit, and social influence are related to a lower chance of introducing bugs.

cs.SE↗

arXiv2019

Software Engineering Research Community Viewpoints on Rapid Reviews

Background: One of the most important current challenges of Software Engineering (SE) research is to provide relevant evidence to practice. In health related fields, Rapid Reviews (RRs) have shown to be an effective method to achieve that goal. However, little is known about how the SE research community perceives the potential applicability of RRs. Aims: The goal of this study is to understand the SE research community viewpoints towards the use of RRs as a means to provide evidence to practitioners. Method: To understand their viewpoints, we invited 37 researchers to analyze 50 opinion statements about RRs, and rate them according to what extent they agree with each statement. Q-Methodology was employed to identify the most salient viewpoints, represented by the so called factors. Results: Four factors were identified: Factor A groups undecided researchers that need more evidence before using RRs; Researchers grouped in Factor B are generally positive about RRs, but highlight the need to define minimum standards; Factor C researchers are more skeptical and reinforce the importance of high quality evidence; Researchers aligned to Factor D have a pragmatic point of view, considering RRs can be applied based on the context and constraints faced by practitioners. Conclusions: In conclusion, although there are opposing viewpoints, there are also some common grounds. For example, all viewpoints agree that both RRs and Systematic Reviews can be poorly or well conducted.

cs.SE↗

arXiv2016

A Comparison of 10 Sampling Algorithms for Configurable Systems

Almost every software system provides configuration options to tailor the system to the target platform and application scenario. Often, this configurability renders the analysis of every individual system configuration infeasible. To address this problem, researchers have proposed a diverse set of sampling algorithms. We present a comparative study of 10 state-of-the-art sampling algorithms regarding their fault-detection capability and size of sample sets. The former is important to improve software quality and the latter to reduce the time of analysis. In a nutshell, we found that sampling algorithms with larger sample sets are able to detect higher numbers of faults, but simple algorithms with small sample sets, such as most-enabled-disabled, are the most efficient in most contexts. Furthermore, we observed that the limiting assumptions made in previous work influence the number of detected faults, the size of sample sets, and the ranking of algorithms. Finally, we have identified a number of technical challenges when trying to avoid the limiting assumptions, which questions the practicality of certain sampling algorithms.

cs.SE↗