On-the-fly generation of K-path tests for C functions

Williams, Nicky; Marre, Bruno; Mouy, Patricia

doi:10.1109/ase.2004.1342749

Cited by 26 publications

(55 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are currently two well established frameworks to achieve automatic test (data) generation from source code: constraint-based testing [8,9,11,25,28] (CBT) and search-based testing [13,14,17,18,19] (SBT). While the former approach focuses on translating (part of) a program into a logical formula whose solutions are relevant test data, the later approach is based on exploring the input space of the program using optimisation-like methods to guide the search toward relevant test data.…”

Section: Introductionmentioning

confidence: 99%

“…We present the three heuristics as enhancements of a (bounded) depth-first search (DFS) path-based procedure, either purely symbolic or concolic. Original path-based testing techniques were based on DFS [11,25,28], while some recent works advocate using other search strategies [7,12,22]. LA, MCD and SF are easy to adapt to these frameworks as well.…”

Section: Item Coverage and Irrelevant Pathsmentioning

confidence: 99%

“…SBT can be local or global, see [17] for an overview. CBT also can be global, translating the whole program in a formula [8,9], or local (path-based) [11,25,28], focusing on a single path.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pruning the Search Space in Path-Based Test Generation

Bardin

Herrmann

2009

2009 International Conference on Software Testing Verification and Validation

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Item Coverage and Irrelevant Pathsmentioning

confidence: 99%

See 1 more Smart Citation

Pruning the Search Space in Path-Based Test Generation

Bardin

Herrmann

2009

2009 International Conference on Software Testing Verification and Validation

View full text Add to dashboard Cite

show abstract

“…The choice of which concrete executions to generate is guided by a constraint solver and path conditions obtained from the symbolic executions. Originating with DART (Godefroid et al 2005) and PathCrawler (Williams et al 2004), a variety of tools and methods have appeared; some of the state-of-the-art tools include CUTE , CREST (Burnim and Sen 2008), and KLEE (Cadar et al 2008) (which evolved from EXE (Cadar et al 2006)) . We wondered whether dynamic symbolic execution could be used instead of random testing for finding noninterference bugs.…”

Section: Related Workmentioning

confidence: 99%

Testing noninterference, quickly

Hriţcu¹,

Lampropoulos

Spector-Zabusky

et al. 2016

J. Funct. Prog.

View full text Add to dashboard Cite

Information-flow control mechanisms are difficult both to design and to prove correct. To reduce the time wasted on doomed proof attempts due to broken definitions, we advocate modern random testing techniques for finding counterexamples during the design process. We show how to use QuickCheck, a property-based random-testing tool, to guide the design of increasingly complex information-flow abstract machines, leading up to a sophisticated register machine with a novel and highly permissive flow-sensitive dynamic enforcement mechanism that is sound in the presence of first-class public labels. We find that both sophisticated strategies for generating well-distributed random programs and readily falsifiable formulations of noninterference properties are critically important for efficient testing. We propose several approaches and evaluate their effectiveness on a collection of injected bugs of varying subtlety. We also present an effective technique for shrinking large counterexamples to minimal, easily comprehensible ones. Taken together, our best methods enable us to quickly and automatically generate simple counterexamples for more than 45 bugs. Moreover, we show how testing guides the discovery of the sophisticated invariants needed for the noninterference proof of our most complex machine.

show abstract

“…During 2004 to 2006 clever implementation of random testing is done to get the benefits of avoidance of infeasible paths and to ignore path selector module [37,67,31]. During this time test data generation using hybrid method that takes the advantages of both static and dynamic method were done [28,66]. The William work path crawler has the advantages because it ignores infeasible paths.…”

Section: Introductionmentioning

confidence: 99%