Automated test generation for engineering applications

Xia, Songtao; Vito, Ben Di; Muñoz, César

doi:10.1145/1101908.1101951

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…In contrast, we use a decision procedure for solving non-linear constraints based on interval semantics. Numerical decision procedures for solving non-linear constraints based on interval search have been used to generate test cases for programs from the automotive and avionics domains from predicate-abstractionbased reachability analysis [31], [32]. Fainekos et al [14] also consider a framework for determining the numerical robustness of simulations of hybrid systems against floatingpoint rounding errors and system modeling uncertainties, based on interval and affine arithmetic.…”

Section: Related Workmentioning

confidence: 99%

Systematic testing for control applications

Majumdar¹,

Saha

Wang

2010

Eighth ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE 2010)

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Software controllers for physical processes are at the core of many safety-critical systems such as avionics, automotive engine control, and process control. Despite their importance, the design and implementation of software controllers remains an art form; dependability is generally poor, and the cost of verifying systems is prohibitive.We illustrate the potential of applying program analysis tools on problems in controller design and implementation by focusing on concolic execution, a technique for systematic testing for software. In particular, we demonstrate how a concolic execution tool can be modified to automatically analyze controller implementations and (a) produce test cases achieving a coverage goal, (b) synthesize ranges for controller variables that can be used to allocate bits in a fixed-point implementation, and (c) verify robustness of an implementation under input uncertainties. We have implemented these algorithms on top of the Splat test generation tool and have carried out preliminary experiments on control software that demonstrates feasibility of the techniques.

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Section: Related Workmentioning

confidence: 99%

Systematic testing for control applications

Majumdar¹,

Saha

Wang

2010

Eighth ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE 2010)

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“…Some work [5], [6] suggest that random testing is same effective as systematic testing techniques. Existing work [7] found that random test case generation achieves less code coverage than systematic generation techniques.…”

Section: Introductionmentioning

confidence: 99%

Java-HCT: An approach to increase MC/DC using Hybrid Concolic Testing for Java programs

Godboley

Dutta

Mohapatra

2016

Annals of Computer Science and Information Systems

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Abstract-Modified Condition / Decision Coverage (MC/DC) is the second strongest coverage criterion in white-box testing. According to DO178C/RTCA criterion it is mandatory to achieve Level A certification for MC/DC. Concolic testing is the combination of Concrete and Symbolic execution. It is a systematic technique that performs symbolic execution but uses randomlygenerated test inputs to initialize the search and to allow the tool to execute programs when symbolic execution fails. In this paper, we extend concolic testing by computing MC/DC using the automatically generated test cases. On the other hand FeedbackDirected Random Test Generation builds inputs incrementally by randomly selecting a method call to apply and find arguments from among previously-constructed inputs. As soon as the input is built, it is executed and checked against a set of contracts and filters.In our proposed work, we combine feedback-directed test cases generation with concolic testing to form Java-Hybrid Concolic Testing (Java-HCT). Java-HCT generates more number of test cases since it combines the features of both FeedbackDirected Random Test and Concolic Testing. Hence, through Java-HCT, we achieve high MC/DC. Combinations of approaches represent different tradeoffs of completeness and scalability. We develop Java-HCT using RANDOOP, jCUTE, and COPECA. Combination of RANDOOP and jCUTE creates more test cases. COPECA is used to measure MC/DC% using the generated test cases. Experimental study shows that Java-HCT produces better MC/DC% than individual testing techniques(feedback-directed random testing and concolic testing). We have improved MC/DC by ×1.62 and by ×1.26 for feedback-directed random testing and concolic testing respectively.

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“…Robustness of hybrid automaton models for control systems have been studied before [9], [10], as have test generation for control software based on symbolic techniques [11], [12]. To the best of our knowledge, the problem of checking robustness in control software and generating tests that exhibit maximal output deviations has not been studied before.…”

Section: Introductionmentioning

confidence: 99%

Symbolic Robustness Analysis

Majumdar

Saha

2009

2009 30th IEEE Real-Time Systems Symposium

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A key feature of control systems is robustness, the property that small perturbations in the system inputs cause only small changes in its outputs. Robustness is key to designing systems that work under uncertain or imprecise environments. While continuous control design algorithms can explicitly incorporate robustness as a design goal, it is not clear if robustness is maintained at the software implementation level of the controller: two "close" inputs can execute very different code paths which may potentially produce vastly different outputs.We present an algorithm and a tool to characterize the robustness of a control software implementation. Our algorithm is based on symbolic execution and non-linear optimization, and computes the maximum difference in program outputs over all program paths when a program input is perturbed. As a by-product, our algorithm generates a set of test vectors which demonstrate the worst-case deviations in outputs for small deviations in inputs. We have implemented our approach on top of the Splat test generation tool and we describe an evaluation of our implementation on two examples of automotive control code.

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Automated test generation for engineering applications

Cited by 12 publications

References 9 publications

Systematic testing for control applications

Systematic testing for control applications

Java-HCT: An approach to increase MC/DC using Hybrid Concolic Testing for Java programs

Symbolic Robustness Analysis

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