A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page

Automated Oracle Generation via Denotational Semantics

Software failure detection is typically done by comparing the running behaviors from a software under test (SUT) against its expected behaviors, called test oracles. In this paper, we present a formal approach to specifying test oracles in denotational semantics for systems with structured inputs. The approach introduces formal semantic evaluation rules, based on the denotational semantics methodology, defined on each productive grammar rule. We extend our grammar-based test generator, GENA, with automated test oracle generation. We provide three case studies of software testing: (i) a benchmark of Java programs on arithmetic calculations, (ii) an open source software on license identification, and (ii) selenium-based web testing. Experimental results demonstrate the effectiveness of our approach and illustrate the success of the application on the software testing

COST Action IC 1402 ArVI: Runtime Verification Beyond Monitoring -- Activity Report of Working Group 1

This report presents the activities of the first working group of the COST Action ArVI, Runtime Verification beyond Monitoring. The report aims to provide an overview of some of the major core aspects involved in Runtime Verification. Runtime Verification is the field of research dedicated to the analysis of system executions. It is often seen as a discipline that studies how a system run satisfies or violates correctness properties. The report exposes a taxonomy of Runtime Verification (RV) presenting the terminology involved with the main concepts of the field. The report also develops the concept of instrumentation, the various ways to instrument systems, and the fundamental role of instrumentation in designing an RV framework. We also discuss how RV interplays with other verification techniques such as model-checking, deductive verification, model learning, testing, and runtime assertion checking. Finally, we propose challenges in monitoring quantitative and statistical data beyond detecting property violation