PINCETTE - Validating Changes and Upgrades in Networked Software

Chockler, Hana; Denaro, Giovanni; Ling, Meijia; Fedyukovich, Grigory; Hyvrinen, A. E. J.; Mariani, Leonardo; Muhammad, Aish; Manuel, Oriol; Rajan, Ajitha; Šerý, Ondřej; Sharygina, Natasha; Tautschnig, Michael

doi:10.1109/csmr.2013.72

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…In the context of the European project PINCETTE [3], we addressed the automatic detection of regression problems by integrating runtime verification, testing, and static analysis, as shown in Fig. 1.…”

Section: Dynamic Analysis Of Regression Problemsmentioning

confidence: 99%

Dynamic Analysis of Regression Problems in Industrial Systems: Challenges and Solutions

Pastore

Mariani

2016

Leveraging Applications of Formal Methods, Verification and Validation: Discussion, Dissemination, Applications

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Section: Dynamic Analysis Of Regression Problemsmentioning

confidence: 99%

Dynamic Analysis of Regression Problems in Industrial Systems: Challenges and Solutions

Pastore

Mariani

2016

Leveraging Applications of Formal Methods, Verification and Validation: Discussion, Dissemination, Applications

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“…These techniques are sound but not complete. Techniques from [18] are used in the PINCETTE project [4]. To curb over approximations, the PINCETTE project also employs dynamic techniques (e.g., concolic testing) to generate regression tests for system upgrades.…”

Section: Related Workmentioning

confidence: 99%

Regression Verification Using Impact Summaries

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Rungta

et al. 2013

Model Checking Software

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Abstract. Regression verification techniques are used to prove equivalence of closely related program versions. Existing regression verification techniques leverage the similarities between program versions to help improve analysis scalability by using abstraction and decomposition techniques. These techniques are sound but not complete. In this work, we propose an alternative technique to improve scalability of regression verification that leverages change impact information to partition program execution behaviors. Program behaviors in each version are partitioned into (a) behaviors impacted by the changes and (b) behaviors not impacted (unimpacted) by the changes. Our approach uses a combination of static analysis and symbolic execution to generate summaries of program behaviors impacted by the differences. We show in this work that checking equivalence of behaviors in two program versions reduces to checking equivalence of just the impacted behaviors. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution; furthermore, our approach can be used with existing approaches to better leverage the similarities between program versions and improve analysis scalability. We evaluate our technique on a set of sequential C artifacts and present preliminary results.

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