Explanation-Based Generalization of Infeasible Path

Delahaye, Mickaël; Botella, Bernard; Gotlieb, Arnaud

doi:10.1109/icst.2010.13

Cited by 19 publications

(19 citation statements)

References 15 publications

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“…Note that buster may not extract the minimal unsatisfied core; however, the extracted core is sufficient to suggest candidate operations. We leave improvements in the implementation to future work, for example, by exploring advanced algorithms for extracting unsatisfied core [9], leveraging other constraint solvers [10], [11], and addressing issues with floating-point computations [12], [13].…”

Section: A Experimental Setupmentioning

confidence: 99%

Test Generation from Business Rules

Jensen¹,

Thummalapenta

Sinha

et al. 2015

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

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Enterprise applications are difficult to test because their intended functionality is either not described precisely enough or described in cumbersome business rules. It takes a lot of effort on the part of a test architect to understand all the business rules and design tests that "cover" them, i.e., exercise all their constituent scenarios. Part of the problem is that it takes a complicated set up sequence to drive an application to a state in which a business rule can even fire. In this paper, we present a business rule modeling language that can be used to capture functional specification of an enterprise system. The language makes it possible to build tool support for rule authoring, so that obvious deficiencies in rules can be detected mechanically. Most importantly, we show how to mechanically generate test sequences-i.e., test steps and test data-needed to exercise these business rules. To this end, we translate the rules into logical formulae and use constraint solving to generate test sequences. One of our contributions is to overcome scalability issues in this process, and we do this by using a novel algorithm for organizing search through the space of candidate sequences to discover covering sequences. Our results on three case studies show the promise of our approach.State st; BalanceType bt = Credit; int crLimit = 100, price = 20; Customer cust = CreateCustomer(st, bt); Customer cust1 = AddCreditLimit(cust, crLimit); Order ord = CreateOrder(cust1); Item item = CreateItem(int price); Order ord1 = AddItemToOrder(ord, item); Invoice inv = GenerateInvoice(ord1);

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Section: A Experimental Setupmentioning

confidence: 99%

Test Generation from Business Rules

Jensen¹,

Thummalapenta

Sinha

et al. 2015

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

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“…If there exists a solution to the computed constraint, it is added to the queue. Lastly, the branch conditions in P 0 are reordered and negated one-by-one (lines [16][17][18][19][20][21][22][23]. Again, if there exists a solution to the computed constraint, it is added to the queue.…”

Section: Interestingly Every Changed Statement In the Relevantmentioning

confidence: 99%

“…In fact, PRV suffers from the same limitations as symbolic execution. Similarly, it can benefit from relevant optimizations such as domain reduction [19], [20], parallelization [21], and better search strategies [22], [23].…”

Section: Empirical Studymentioning

confidence: 99%

Partition-based regression verification

Böhme¹,

Oliveira²,

Roychoudhury³

2013

2013 35th International Conference on Software Engineering (ICSE)

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Abstract-Regression verification (RV) seeks to guarantee the absence of regression errors in a changed program version. This paper presents Partition-based Regression Verification (PRV): an approach to RV based on the gradual exploration of differential input partitions. A differential input partition is a subset of the common input space of two program versions that serves as a unit of verification. Instead of proving the absence of regression for the complete input space at once, PRV verifies differential partitions in a gradual manner. If the exploration is interrupted, PRV retains partial verification guarantees at least for the explored differential partitions. This is crucial in practice as verifying the complete input space can be prohibitively expensive.Experiments show that PRV provides a useful alternative to state-of-the-art regression test generation techniques. During the exploration, PRV generates test cases which can expose different behaviour across two program versions. However, while test cases are generally single points in the common input space, PRV can verify entire partitions and moreover give feedback that allows programmers to relate a behavioral difference to those syntactic changes that contribute to this difference.

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“…Balakrishnan et al [2] use this technique in the context of abstract interpretation. Delahaye et al [11] present a technique how to generalize infeasible paths. However they have not investigated its use in static analysis.…”

Section: Related Workmentioning

confidence: 99%

SMT-Based False Positive Elimination in Static Program Analysis

Junker

Huuck

Fehnker

et al. 2012

Formal Methods and Software Engineering

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Abstract. Static program analysis for bug detection in large C/C++ projects typically uses a high-level abstraction of the original program under investigation. As a result, so-called false positives are often inevitable, i.e., warnings that are not true bugs. In this work we present a novel abstraction refinement approach to automatically investigate and eliminate such false positives. Central to our approach is to view static analysis as a model checking problem, to iteratively compute infeasible sub-paths of infeasible paths using SMT solvers, and refine our models by adding observer automata to exclude such paths. Based on this new framework we present an implementation of the approach into the static analyzer Goanna and discuss a number of real-life experiments on larger C code projects, demonstrating that we were able to remove most false positives automatically.

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Explanation-Based Generalization of Infeasible Path

Cited by 19 publications

References 15 publications

Test Generation from Business Rules

Test Generation from Business Rules

Partition-based regression verification

SMT-Based False Positive Elimination in Static Program Analysis

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