Tassio Guerreiro Antunes Virginio
@professor
ifto.edu.br
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Railana Santana, Luana Martins, Tássio Virgínio, Larissa Rocha, Heitor Costa, and Ivan Machado
Elsevier BV
Railana Santana, Luana Martins, Larissa Rocha, Tássio Virgínio, Adriana Cruz, Heitor Costa, and Ivan Machado
ACM
Test smells are fragments of code that can affect the comprehensibility and the maintainability of the test code. Preventing, detecting, and correcting test smells are tasks that may require a lot of effort, and might not scale to large-sized projects when carried out manually. Currently, there are many tools available to support test smells detection. However, they usually do not provide neither a user-friendly interface nor automated support for refactoring the test code to remove test smells. In this work, we propose RAIDE, an open-source and IDE-integrated tool. RAIDE assists testers with an environment for automated detection of lines of code affected by test smells, as well as a semi-automated refactoring for Java projects using the JUnit framework.
Tássio Virgínio, Luana Almeida Martins, Larissa Rocha Soares, Railana Santana, Heitor Costa, and Ivan Machado
ACM
Developing test code can be as or more expensive than developing production code. Commonly, developers use automated unit test generators to speed up software testing. The purpose of such tools is to shorten production time without decreasing code quality. Nonetheless, unit tests usually do not have a quality check layer above testing code, which might be hard to guarantee the quality of the generated tests. A strategy to verify the tests quality is to analyze the presence of test smells in test code. Test smells are characteristics in the test code that possibly indicate weaknesses in test design and implementation. Their presence could be used as a quality indicator. In this paper, we present an empirical study to analyze the quality of unit test code generated by automated test tools. We compare the tests generated by two tools (Randoop and Evo- Suite) with the existing unit test suite of twenty-one open-source Java projects. We analyze the unit test code to detect the presence of nineteen types of test smells. The results indicated significant differences in the unit test quality when comparing data from the automated unit test generators and existing unit test suites.
Tássio Virgínio, Luana Martins, Larissa Rocha, Railana Santana, Adriana Cruz, Heitor Costa, and Ivan Machado
ACM
Several strategies have been proposed for test quality measurement and analysis. Code coverage is likely the most widely used one. It enables to verify the ability of a test case to cover as many source code branches as possible. Although code coverage has been widely used, novel strategies have been recently employed. It is the case of test smells analysis, which has been introduced as an affordable strategy to evaluate the quality of test code. Test smells are poor design choices in implementation, and their occurrence in test code might reduce the quality of test suites. Test smells identification is clearly dependent on tool support, otherwise it could become a cost-ineffective strategy. However, as far as we know, there is no tool that combines code coverage and test smells to address test quality measurement. In this work, we present the JNose Test, a tool aimed to analyze test suite quality in the perspective of test smells. JNose Test detects code coverage and software evolution metrics and a set of test smells throughout software versions.
Tássio Virgínio, Railana Santana, Luana Almeida Martins, Larissa Rocha Soares, Heitor Costa, and Ivan Machado
ACM
Software testing is a key practice in the software quality assurance process. Usually, the quality of a test is not analyzed before its execution, i.e., there are no tests to check the tests. When the quality of tests is not guaranteed, it may impair the quality of the software. Test Smells are an alternative to indicate problems in the test code that can affect test maintainability, more specifically readability and comprehension. This study investigates correlations between test coverage and test smells types. We also introduce the JNose Test, a tool to automate test smells detection. We analyzed 11 open source projects and detected 21 types of smells and 10 different test coverage metrics to each test class. We identified 63 out of 210 calculated correlations. Our results show that there is a relationship between test smells and test coverage, in which test smells may influence code coverage. Our findings might support software testers and help them understand the behavior and consequences of poorly written and designed tests.