An empirical investigation into branch coverage for C programs using CUTE and AUSTIN

Automated test data generation has remained a topic of considerable interest for several decades because it lies at the heart of attempts to automate the process of Software Testing. This paper reports the results of an empirical study using the dynamic symbolic-execution tool. CUTE, and a search based tool, AUSTIN on five non-trivial open source applications. The aim is to provide practitioners with an assessment of what can be achieved by existing techniques with little or no specialist knowledge and to provide researchers with baseline data against which to measure subsequent work. To achieve this, each tool is applied 'as is', with neither additional tuning nor supporting harnesses and with no adjustments applied to the subject programs under test. The mere fact that these tools can be applied 'out of the box' in this manner reflects the growing maturity of Automated test data generation. However, as might be expected, the study reveals opportunities for improvement and suggests ways to hybridize these two approaches that have hitherto been developed entirely independently. (C) 2010 Elsevier Inc. All rights reserved

A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation

Otsingupõhine tarkvara testimine kasutab metaheuristilisi algoritme, et automatiseerida testide genereerimist. Selle töö eesmärgiks on osaliselt taasluua 2010. aastal kirjutatud Ali et al. artikkel, et uurida, kuidas on aastatel 2008-2015 kasutatud metaheuristilisi algoritme testide loomiseks. See töö analüüsib, kuidas on antud artiklid koostatud ning kuidas neis on algoritmide maksumust ja efektiivsust hinnatud. Kogutud tulemusi võrreldakse Ali et al. tulemustega.Search based software testing uses metaheuristic algorithms to automate the generation of test cases. This thesis partially replicates a literature study published in 2010 by Ali et al. to determine how studies published in 2008-2015 use metaheuristic algorithms to automate the generation of test cases. The thesis analyses how these studies were conducted and how the cost-effectiveness is assessed in these papers. The trends detected in the new publications are compared to those presented in Ali et al

Dorylus: An Ant Colony Based Tool for Automated Test Case Generation

Automated test generation to cover all branches within a program is a hard task. We present Dorylus, a test suite generation tool that uses ant colony optimisation, guided by coverage. Dorylus constructs a continuous domain over which it conducts independent, multiple objective search that employs a lightweight, dynamic, path-based input dependency analysis. We compare Dorylus with EvoSuite with respect to both coverage and speed using two corpora. The first benchmark contains string based programs, where our results demonstrate that Dorylus improves over EvoSuite on branch coverage and is 50% faster on average. The second benchmark consists of 936 Java programs from SF110 and suggests Dorylus generalises well as it achieves 79% coverage on average whereas the best performing of three EvoSuite algorithms reaches 89%

Conformal Prediction Regions for Time Series using Linear Complementarity Programming

Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction to time series data leads to conservative prediction regions. In fact, to obtain prediction regions over $T$ time steps with confidence $1-\delta$, {previous works require that each individual prediction region is valid} with confidence $1-\delta/T$. We propose an optimization-based method for reducing this conservatism to enable long horizon planning and verification when using learning-enabled time series predictors. Instead of considering prediction errors individually at each time step, we consider a parameterized prediction error over multiple time steps. By optimizing the parameters over an additional dataset, we find prediction regions that are not conservative. We show that this problem can be cast as a mixed integer linear complementarity program (MILCP), which we then relax into a linear complementarity program (LCP). Additionally, we prove that the relaxed LP has the same optimal cost as the original MILCP. Finally, we demonstrate the efficacy of our method on a case study using pedestrian trajectory predictors

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E