The modernization of a software system is a complex and expensive task and requires a deep understanding of the existing system. The capability of re-factoring a complex application into some high-level views is mandatory to elicit its structure and start localize possible changes. The high number of different implementation technologies imposes a model-based, neutral approach to reconstruct the structure and hide unnecessary details. OMG supports this view and proposes KDM (Knowledge Discovery Meta model) as means to describe software systems in detail, but unfortunately KDM supports a component-oriented decomposition of the system of interest only partially. To bypass this limitation, the paper proposes the COMO (Component-Oriented MOdernization) meta model to extend KDM, by borrowing recurring concepts from component-based solutions and software architectures, and to support a proper componentization of the system we want to modernize. The paper presents the main elements of the COMO meta model and exemplifies them on a simple case study.

TestFul is a novel evolutionary testing approach for object-oriented programs with complex internal states. In our preliminary experiments, it already outperformed some of the well-known search-based testing approaches. In this paper we show how TestFul can be further improved by leveraging three efficiency enhancement techniques: seeding, hybridization, and fitness inheritance. We considered four extensions of TestFul: three using each enhancement separately, and one using all of them at the same time. We used these new versions of TestFul to generate tests for six Java classes taken from the literature, public software libraries, and third party benchmarks. We compared the performance of the original TestFul against these new versions. Our results show that each enhancement technique results in a significant speed-up and, even more interesting, the highest improvement is achieved when all the enhancements are combined together.

This paper presents TestFul , an Eclipse plugin for the generation of tests for Java classes. It is based on the idea of search-based testing, working both at class and method level. The former puts objects in useful states, used by the latter to exercise the uncovered parts of the class.

This paper presents TestFul, an evolutionary testing approach for Java classes that works both at class and method level. TestFul exploits a multi-objective evolutionary algorithm to identify the “best” tests. The paper introduces the main elements of TestFul. It also compares TestFul against well-known search-based solutions using a set of classes taken from literature, known software libraries, and independent testing benchmarks. The comparison considers statement and branch coverage, size of generated tests, and generation time. On considered classes, TestFul generates better tests than other search-based solutions, and achieves higher structural coverages with tests small enough to be usable.

This paper introduces TestFul, a framework for testing stateful systems and focuses on object-oriented software. TestFul employs a hybrid multi-objective evolutionary algorithm, to explore the space of feasible tests efficiently, and novel quality metrics, based on both def-use pairs and behavioral coverage, to judge the quality of tests. We compare our framework against random testing by considering the level of coverage, the size of generated tests, and the time required to generate the tests. Our preliminary results show the validity of the approach: TestFul outperforms random testing in most of the cases.