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To find defects in software, one needs test cases that execute the software systematically, and oracles that assess the correctness of the observed behavior when running these test cases. This paper presents EvoSuite, a tool that automatically generates test cases with assertions for classes written in Java code. To achieve this, EvoSuite applies a novel(More)
Not all bugs lead to program crashes, and not always is there a formal specification to check the correctness of a software test's outcome. A common scenario in software testing is therefore that test data are generated, and a tester manually adds test oracles. As this is a difficult task, it is important to produce small yet representative test sets, and(More)
To assess the quality of test suites, mutation analysis seeds artificial defects (mutations) into programs; a non-detected mutation indicates a weakness in the test suite. We present an automated approach to generate unit tests that detect these mutations for object-oriented classes. This has two advantages: First, the resulting test suite is optimized(More)
A good test suite is one that detects real faults. Because the set of faults in a program is usually unknowable, this definition is not useful to practitioners who are creating test suites, nor to researchers who are creating and evaluating tools that generate test suites. In place of real faults, testing research often uses mutants, which are artificial(More)
Search-based techniques have been shown useful for the task of generating tests, for example in the case of object-oriented software. But, as for any meta-heuristic search, the efficiency is heavily dependent on many different factors, seeding is one such factor that may strongly influence this efficiency. In this paper, we evaluate new and typical(More)
Many software engineering problems have been addressed with search algorithms. Search algorithms usually depend on several parameters (e.g., population size and crossover rate in genetic algorithms), and the choice of these parameters can have an impact on the performance of the algorithm. It has been formally proven in the No Free Lunch theorem that it is(More)
Multiobjective Evolutionary Algorithms are increasingly used to solve optimization problems in software engineering. The choice of parameters for those algorithms usually follows the "default" settings, often accepted as "rule of thumb" or common wisdom. The fact is that each algorithms needs to be tuned for the problem at hand. Previous work [Arcuri and(More)
Test generation tools commonly aim to cover structural artefacts of software, such as either the source code or the user interface. However, focusing only on source code can lead to unrealistic or irrelevant test cases, while only exploring a user interface often misses much of the underlying program behavior. Our EXSYST prototype takes a new approach by(More)
Recent advances in software testing allow automatic derivation of tests that reach almost any desired point in the source code. There is, however, a fundamental problem with the general idea of targeting one distinct test coverage goal at a time: Coverage goals are neither independent of each other, nor is test generation for any particular coverage goal(More)
Container classes such as lists, sets, or maps are elementary data structures common to many programming languages. Since they are a part of standard libraries, they are important to test, which led to research on advanced testing techniques targeting such containers and research on comparing testing techniques using such containers. However, these(More)