Test smells are symptoms in the test code that indicate possible design or implementation problems. Previous research demonstrated their harmfulness and the developers' acknowledgment of test smells' ...effects, prevention, and refactoring strategies. Test automation frameworks are constantly evolving, and the JUnit, one of the most used ones for Java projects, has its version 5 available since late 2017. However, we do not know the extent to which developers use the newly introduced features and whether such features indeed help refactor existing test code to remove test smells. This article conducts a mixed-method study investigation to minimize these knowledge gaps. Our study consists of three parts. First, we evaluate the usage of this framework and its features by analyzing the source code of 485 popular Java open-source projects on GitHub that use JUnit. We found that 15.9% of these projects use the JUnit 5 library. We also found that, from 17 new features detected in use, only 3 (i.e., 17.6%) are responsible for more than 70% of usages, limiting optimized propositions to test code creation and maintenance. Second, after identifying features in the JUnit 5 framework that could be considered to test smells removal and prevention, we use these features to propose novel refactorings. In particular, we present refactorings based on 7 introduced JUnit 5 features that help to remove 13 test smells, such as Assertion Roulette, Test Code Duplication, and Conditional Test Logic. Third, to evaluate our refactorings with the opinions of experienced developers, we (i) survey 212 developers for their preferences and comments about our refactorings, corroborating the benefits of our proposals and raising community feedback on JUnit 5 features, and (ii) we refactor actual test code from popular GitHub Java projects and submit 38 Pull Requests, reaching a 94% acceptance rate among respondents. As implications of our study, we alert the software testing community (i.e., practitioners and researchers) to the need to study the JUnit 5 features to effectively remove and prevent test smells. To better assist this process, we give directions on how test smells can be refactored using such features.
Applying Machine learning algorithms to data streams is a challenging task because traditional strategies suppose datasets to be labeled, finite and stationary. In the context of data streams, where ...the data is generated in real-time and the labels may be missing due to the high cost of the labeling process, the proposal of semi-supervised learning (SSL) strategies to learn from labeled and unlabeled data at the same time seems to be a viable solution, despite also being challenging. In this paper, we present a novel approach to handle missing labels for classification learning in data streams, named co-op training, which is based on self-training incremental and co-training. In a controlled experiment, we execute the proposed algorithm, along with most well-known semi-supervised learning strategies, in 11 artificial and real-world datasets, and compare the results. We found our strategy to be more accurate than the other SSL algorithms in most datasets, also presenting better run-times when accuracies were similar. These methods are implemented in the Massive Online Analysis (MOA) open-source software as an internal benchmark component, to help researchers to run experimental comparisons on semi-supervised learning on data streams easily.
Recently, some initiatives to start the so-called semantic web-based educational systems (SWBES) have emerged in the field of artificial intelligence in education (AIED). The main idea is to ...incorporate semantic web resources to the design of AIED systems aiming to update their architectures to provide more adaptability, robustness and richer learning environments. However, the construction of such systems is highly complex and faces several challenges in terms of software engineering and artificial intelligence aspects. This paper presents a computational model for developing SWBES focusing on the problem of how to make the development easier and more useful for both developers and authors. In order to illustrate the features of the proposed model, a case study is presented. Furthermore, a discussion about the results regarding the computational model construction is available.
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%
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.
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.
Refactoring Test Smells Soares, Elvys; Ribeiro, Márcio; Amaral, Guilherme ...
Proceedings of the 5th Brazilian Symposium on Systematic and Automated Software Testing,
10/2020
Conference Proceeding
Test smells are symptoms in the test code that indicate possible design or implementation problems. Their presence, along with their harmfulness, has already been demonstrated by previous researches. ...However, we do not know to what extent developers acknowledge the presence of test smells and how to refactor existing code to eliminate them in practice. This study aims to assess open-source developers' awareness about the existence of test smells and their refactoring strategies. We conducted a mixed-method study with two parts: (i) a survey with 73 experienced open-source developers to assess their preference and motivation to choose between 10 different smelly test code samples, found in 272 open-source projects, and their refactored versions; and (ii) the submission of 50 pull requests to assess developers' acceptance of the proposed refactorings. As a result, most surveyed developers preferred the refactored proposal for 78% of the investigated test smells, and the pull requests had an average acceptance of 75% among respondents. Additionally, we were able to provide empiric validation for literature-proposed refactoring strategies. This study demonstrates that although not always using the academic terminology, developers acknowledge both the negative impact of test smells presence and most of the literature's proposals for their removal.
Mutation Operators for Java Streams Aranda III, Manoel; Soares, Elvys; Ribeiro, Márcio ...
Proceedings of the 7th Brazilian Symposium on Systematic and Automated Software Testing,
10/2022
Conference Proceeding
Mutation testing analyzes test suites to verify their capability to detect artificially injected faults. Mutation testing tools rely on mutation operators to simulate those faults by modifying ...language constructs. The popularization of Streaming APIs, which enable parallel processing of native data structures with relatively succinct constructs, presents challenges related to functional programming, and faults from the APIs’ misuse are already objects of study. However, no comprehensive mutation operators have been defined for this purpose. We propose seven mutation operators to simulate stream-related faults. To evaluate our operators, we mined 22 open-source projects from different domains (i.e., applications for smart cities and messaging frameworks) to identify faults our operators could simulate. We analyzed 357 commits, raising 91 fixes for stream-related faults in GitHub Java projects. Our operators can simulate 96.7% of the analyzed faults, and we verified five of our proposals in practice. Our mutation operators can enhance the capabilities of current mutation testing tools and help developers to improve their test suites by avoiding stream-related faults.
The Semantic Web is an extension of the current Web, where the availability of information
is expected to enable the cooperation between man and, above all, machines. The creation
of standards which ...express shared meaning enable the construction of applications to solve
integration, collaboration and automation problems which were already been identified by scientific
community and technology consumers.
The use of Web Services has brought several advances in this sense, and their annotation
in semantic terms, transforming them into Semantic Web Services, enables the Semantic Web
intent. Several technologies also enable the creation of such elements and their inherent use as
basic blocks of application development whose scope is embedded on Web. This way, due to
the fast growing of the number of services, some approaches to effectively solve the problem
of services integration and use become necessary.
This work proposes a modeling of a software solution to the discovery and composition
of Semantic Web Services problem through the use of a genetic algorithm based on abstract
data types. It is also proposed a tool implementation using OWL, OWL-S and OWL-S API
languages and frameworks as well as the formal problem definition along with the scientific
community expectations to the given solution.
AWeb Semântica é uma ampliação da web atual onde a disposição da informação viabiliza
a cooperação entre homens e, sobretudo, entre máquinas. O surgimento de padrões web que
expressam significado compartilhado possibilitam a construção de aplicações que resolvem
problemas de integração, colaboração e automação já identificados pela comunidade científica
e mercado consumidor de tecnologias.
A utilização de Serviços Web trouxe grandes ganhos neste sentido, e sua anotação em
termos semânticos, tornando-os Serviços Web Semânticos, viabiliza a proposta da Web Semântica.
Diversas tecnologias viabilizam a construção de tais elementos e sua conseqüente
utilização como blocos básicos do desenvolvimento de aplicações cujo escopo é embarcado
na web. Assim, dado o rápido crescimento da quantidade de serviços, tornam-se necessárias
abordagens que resolvam de forma efetiva, com garantias de qualidade e tempo de resposta
aceitável, a integração e posterior utilização destes.
Este trabalho propõe a modelagem de uma solução de software para o problema da Descoberta
e Composição de Serviços Web Semânticos através do uso do Algoritmo Genético
Baseado em Tipos Abstratos de Dados. Também é proposta uma implementação utilizando
OWL, OWL-S e a OWL-S API. São apresentadas a definição formal do problema, as expectativas
da comunidade científica quanto às soluções elaboradas e os resultados obtidos com
respeito à viabilidade da proposta.
