Dynamic data flow testing

Data flow testing is a particular form of testing that identifies data flow relations as test objectives. Data flow testing has recently attracted new interest in the context of testing object oriented systems, since data flow information is well suited to capture relations among the object states, and can thus provide useful information for testing method interactions. Unfortunately, classic data flow testing, which is based on static analysis of the source code, fails to identify many important data flow relations due to the dynamic nature of object oriented systems. This thesis presents Dynamic Data Flow Testing, a technique which rethinks data flow testing to suit the testing of modern object oriented software. Dynamic Data Flow Testing stems from empirical evidence that we collect on the limits of classic data flow testing techniques. We investigate such limits by means of Dynamic Data Flow Analysis, a dynamic implementation of data flow analysis that computes sound data flow information on program traces. We compare data flow information collected with static analysis of the code with information observed dynamically on execution traces, and empirically observe that the data flow information computed with classic analysis of the source code misses a significant part of information that corresponds to relevant behaviors that shall be tested. In view of these results, we propose Dynamic Data Flow Testing. The technique promotes the synergies between dynamic analysis, static reasoning and test case generation for automatically extending a test suite with test cases that execute the complex state based interactions between objects. Dynamic Data Flow Testing computes precise data flow information of the program with Dynamic Data Flow Analysis, processes the dynamic information to infer new test objectives, which Dynamic Data Flow Testing uses to generate new test cases. The test cases generated by Dynamic Data Flow Testing exercise relevant behaviors that are otherwise missed by both the original test suite and test suites that satisfy classic data flow criteria

Dynamic data flow testing of object oriented systems

Data flow testing has recently attracted new interest in the context of testing object oriented systems, since data flow information is well suited to capture relations among the object states, and can thus provide useful information for testing method interactions. Unfortunately, classic data flow testing, which is based on static analysis of the source code, fails to identify many important data flow relations due to the dynamic nature of object oriented systems. In this paper, we propose a new technique to generate test cases for object oriented software. The technique exploits useful inter-procedural data flow information extracted dynamically from execution traces for object oriented systems. The technique is designed to enhance an initial test suite with test cases that exercise complex state based method interactions. The experimental results indicate that dynamic data flow testing can indeed generate test cases that exercise relevant behaviors otherwise missed by both the original test suite and by test suites that satisfy classic data flow criteria

Search-based data-flow test generation

Abstract—Coverage criteria based on data-flow have long been discussed in the literature, yet to date they are still of surprising little practical relevance. This is in part because 1) manually writing a unit test for a data-flow aspect is more challenging than writing a unit test that simply covers a branch or statement, 2) there is a lack of tools to support data-flow testing, and 3) there is a lack of empirical evidence on how well data-flow testing scales in practice. To overcome these problems, we present 1) a search-based technique to automatically generate unit tests for data-flow criteria, 2) an implementation of this technique in the EVOSUITE test generation tool, and 3) a large empirical study applying this tool to the SF100 corpus of 100 open source Java projects. On average, the number of coverage objectives is three times as high as for branch coverage. However, the level of coverage achieved by EVOSUITE is comparable to other criteria, and the increase in size is only 15%, leading to higher mutation scores. These results counter the common assumption that data-flow testing does not scale, and should help to re-establish data-flow testing as a viable alternative in practice. Keywords-data-flow coverage, search based testing, unit testing I

Combining Multiple Coverage Criteria in Search-Based Unit Test Generation

Automated test generation techniques typically aim at max- imising coverage of well-established structural criteria such as statement or branch coverage. In practice, generating tests only for one speci c criterion may not be su cient when testing object oriented classes, as standard structural coverage criteria do not fully capture the properties developers may desire of their unit test suites. For example, covering a large number of statements could be easily achieved by just calling the main method of a class; yet, a good unit test suite would consist of smaller unit tests invoking individual methods, and checking return values and states with test assertions. There are several di erent properties that test suites should exhibit, and a search-based test generator could easily be extended with additional tness functions to capture these properties. However, does search-based testing scale to combinations of multiple cri- teria, and what is the e ect on the size and coverage of the resulting test suites? To answer these questions, we extended the EvoSuite unit test generation tool to support combinations of multiple test criteria, de ned and implemented several di erent criteria, and applied combinations of criteria to a sample of 650 open source Java classes. Our experiments suggest that optimising for several criteria at the same time is feasible without increasing computational costs: When combining nine di erent criteria, we observed an average decrease of only 0.4% for the constituent coverage criteria, while the test suites may grow up to 70%