Stateful Testing: Finding More Errors in Code and Contracts

Automated random testing has shown to be an effective approach to finding faults but still faces a major unsolved issue: how to generate test inputs diverse enough to find many faults and find them quickly. Stateful testing, the automated testing technique introduced in this article, generates new test cases that improve an existing test suite. The generated test cases are designed to violate the dynamically inferred contracts (invariants) characterizing the existing test suite. As a consequence, they are in a good position to detect new errors, and also to improve the accuracy of the inferred contracts by discovering those that are unsound. Experiments on 13 data structure classes totalling over 28,000 lines of code demonstrate the effectiveness of stateful testing in improving over the results of long sessions of random testing: stateful testing found 68.4% new errors and improved the accuracy of automatically inferred contracts to over 99%, with just a 7% time overhead.Comment: 11 pages, 3 figure

Automatic Test Generation for Space

The European Space Agency (ESA) uses an engine to perform tests in the Ground Segment infrastructure, specially the Operational Simulator. This engine uses many different tools to ensure the development of regression testing infrastructure and these tests perform black-box testing to the C++ simulator implementation. VST (VisionSpace Technologies) is one of the companies that provides these services to ESA and they need a tool to infer automatically tests from the existing C++ code, instead of writing manually scripts to perform tests. With this motivation in mind, this paper explores automatic testing approaches and tools in order to propose a system that satisfies VST needs

Ontology-based data semantic management and application in IoT- and cloud-enabled smart homes

The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning

Generating target system specifications from a domain model using CLIPS

The quest for reuse in software engineering is still being pursued and researchers are actively investigating the domain modeling approach to software construction. There are several domain modeling efforts reported in the literature and they all agree that the components that are generated from domain modeling are more conducive to reuse. Once a domain model is created, several target systems can be generated by tailoring the domain model or by evolving the domain model and then tailoring it according to the specified requirements. This paper presents the Evolutionary Domain Life Cycle (EDLC) paradigm in which a domain model is created using multiple views, namely, aggregation hierarchy, generalization/specialization hierarchies, object communication diagrams and state transition diagrams. The architecture of the Knowledge Based Requirements Elicitation Tool (KBRET) which is used to generate target system specifications is also presented. The preliminary version of KBRET is implemented in the C Language Integrated Production System (CLIPS)

Exploring the link between test suite quality and automatic specification inference

While no one doubts the importance of correct and complete specifications, many industrial systems still do not have formal specifications written out — and even when they do, it is hard to check their correctness and completeness. This work explores the possibility of using an invariant extraction tool such as Daikon to automatically infer specifications from available test suites with the idea of aiding software engineers to improve the specifications by having another version to compare to. Given that our initial experiments did not produce satisfactory results, in this paper we explore which test suite attributes influence the quality of the inferred specification. Following further study, we found that instruction, branch and method coverage are correlated to high recall values, reaching up to 97.93%.peer-reviewe

Completing and adapting models of biological processes

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 1Red de Universidades con Carreras en Informática (RedUNCI