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10 results for “automatic test generation”
Evaluation Data of the Implementation of the Approach for Automatic Test Generation for Information-Flow Properties
<p>This data set contains the programs for which the automatic test generation approach of the KeY theorem prover was used to automatically generate noninterference tests.</p> <p>The approach is described in <a href="http://dx.doi.org/10.1145/3297280.3297500 ">http://dx.doi.org/10.1145/3297280.3297500 </a></p> <p>DATA<br> ---------<br> The data folder contains the secure and insecure programs which were evaluated and the tests which were generated for them.</p> <p>Each program is in the folder "program" and is written in Java and specified in an extended version of the JML specification language. Check out <a href="http://dx.doi.org/10.5445/IR/1000046878">http://dx.doi.org/10.5445/IR/1000046878</a> for a reference on the used specification language.</p> <p>For each example we provide the tests that were generated. For the insecure examples we provide the tests generated with each of the two options of our approach. The tests generated with the option for searching for counterexamples is in the folder "WithPost" of each insecure example.</p> <p> </p>
Replication package of "Revisiting Test Smells in Automatically Generated Tests: Limitations, Pitfalls, and Opportunities"
<p><strong>Abstract:</strong><br> Test smells attempt to capture design issues in test code that reduce their maintainability. Previous work found such smells to be highly common in automatically generated test-cases, but based this result on specific static detection rules; although these are based on the original definition of “test smells”, a recent empirical study showed that developers perceive these as overly strict and non-representative of the maintainability and quality of test suites. This leads us to investigate how effective such test smell detection tools are on automatically generated test suites. In this paper, we build a dataset of 2,340 test cases automatically generated by EVOSUITE for 100 Java classes. We performed a multi-stage, cross-validated manual analysis to identify six types of test smells and label their instances. We benchmark the performance of two test smell detection tools: one widely used in prior work, and one recently introduced with the express goal to match developer perceptions of test smells. Our results show that these test smell detection strategies poorly characterized the issues in automatically generated test suites; the older tool’s detection strategies, especially, misclassified over 70% of test smells, both missing real instances (false negatives) and marking many smell-free tests as smelly (false positives). We identify common patterns in these tests that can be used to improve the tools, refine and update the definition of certain test smells, and highlight as of yet uncharacterized issues. Our findings suggest the need for (i) more appropriate metrics to match development practice; and (ii) more accurate detection strategies, to be evaluated primarily in industrial contexts.</p>
Online Appendix - Scented Since the Beginning: On the Diffuseness of Test Smells in Automatically Generated Test Code
<p>Online appendix for the paper "Scented Since the Beginning: On the Diffuseness of Test Smells in Automatically Generated Test Code".</p> <p>The full description of the content of this appendix can be found in the README file.</p>
Developer-Centric Test Amplification: The Interplay Between Automatic Generation and Human Exploration --- Appendix
<p>This online appendix contains the accumulated code occurences during the interviews performed to evaluate our developer-centric test amplification approach and the TestCube plugin. In addition, it documents the inter-rater-reliability analysis we performed.</p>
Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions - Dataset and Results
<p>This dataset contains the replication package for the paper: Federico Formica, Tony Fan, and Claudio Menghi. 2023. "Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions".</p> <p>The dataset contains:</p> <ul> <li>The models used in the evaluation section.</li> <li>All the results obtained by running Athena-S on the models.</li> <li>The scripts that automatically analyze the results file and produce the tables and figures used in the paper.</li> </ul>
Hybrid ATPG (Automatic Test Pattern Generation) algorithm
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An empirical study of automatically-generated tests from the perspective of test smells
<p>Developing software test code can be as or more expensive than developing software 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. An emerging strategy to verify the tests quality is to analyze the presence of test smells in software test code. Test smells are characteristics in the test code that possibly indicate weaknesses in test design and implementation. The presence of test smells in unit test code could be used as an indicator of unit test quality. In this paper, we present an empirical study aimed to analyze the quality of unit test code generated by automated test tools. We compare the tests generated by two tools (Randoop and EvoSuite) with the existing unit test suite of open-source software projects. We analyze the unit test code of twenty-one open-source Java projects and detected the presence of nineteen types of test smells. The results indicated significant differences in the unit test quality when comparing data from both automated unit test generators and existing unit test suites.</p>
A Large Scale Study On the Effectiveness of Manual and Automatic Unit Test Generation
<p>Recently, an increasingly large amount of effort has been devoted to implementing tools to generate unit test suites automatically. Previous studies have investigated the effectiveness of these tools by comparing automatically generated test suites (ATSs) to manually written test suites (MTSs). Most of these studies report that ATSs can achieve higher code coverage, or even mutation coverage, than MTSs, particularly when suites are generated from defective code. However, these studies usually consider a limited amount of classes or subject programs, while the adoption of such tools in the industry is still low. This work aims to compare the effectiveness of ATSs and MTSs when applied as regression test suites. We conduct an empirical study, using ten programs (1368 classes), written in Java, that already have MTSs and apply two sophisticated tools that automatically generate test cases: Randoop and EvoSuite. To evaluate the test suites’ effectiveness, we use line and mutation coverage. Our results indicate that MTSs are, in general, more effective than ATSs regarding the investigated metrics. Moreover, the number of generated test cases may not indicate test suites’ effectiveness. Furthermore, there are situations when ATSs are more effective, and even when ATSs and MTSs can be complementary.</p>
An empirical study of automatically-generated tests from the perspective of test smells
<p>Developing software test code can be as or more expensive than developing software 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 software test code. Test smells are characteristics in the test code that possibly indicate weaknesses in test design and implementation. Their presence in test code 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 EvoSuite) 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.</p>
Dataset of the Automatic Unit Test Generation for Programming Assignments Using Large Language Models
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