PROJECT TITLE :
Instance Generator and Problem Representation to Improve Object Oriented Code Coverage
Search-based approaches have been extensively applied to unravel the matter of software test-information generation. Yet, test-data generation for object-oriented programming (OOP) is challenging due to the options of OOP, e.g., abstraction, encapsulation, and visibility that stop direct access to some elements of the supply code. To deal with this downside we tend to present a new automated search-based software test-information generation approach that achieves high code coverage for unit-class testing. We have a tendency to initial describe how we tend to structure the check-knowledge generation drawback for unit-category testing to generate relevant sequences of methodology calls. Through a static analysis, we have a tendency to take into account only methods or constructors changing the state of the category-underneath-check or which will reach a test target. Then we have a tendency to introduce a generator of instances of classes that is based on a family of means-of-instantiation as well as subclasses and external factory ways. It conjointly uses a seeding strategy and a diversification strategy to extend the chance to reach a test target. Employing a search heuristic to achieve all check targets at the identical time, we implement our approach in a tool, JTExpert, that we evaluate on additional than 100 Java classes from totally different open-supply libraries. JTExpert provides better results in terms of search time and code coverage than the state-of-the-art, EvoSuite, that uses ancient techniques.
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