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Martin Schäf

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

LPAR Conference 2017 Conference Paper

Quantified Heap Invariants for Object-Oriented Programs

Temesghen Kahsai
Rody Kersten
Philipp Rümmer
Martin Schäf

Heap and data structures represent one of the biggest challenges when applying model checking to the analysis of software programs: in order to verify (unbounded) safety of a program, it is typically necessary to formulate quantified inductive invariants that state properties about an unbounded number of heap locations. Methods like Craig interpolation, which are commonly used to infer invariants in model checking, are often ineffective when a heap is involved. To address this challenge, we introduce a set of new proof and program transformation rules for verifying object-oriented programs with the help of space invariants, which (implicitly) give rise to quantified invariants. Leveraging advances in Horn solving, we show how space invariants can be derived fully automatically, and how the framework can be used to effectively verify safety of Java programs.

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LPAR Conference 2015 Conference Paper

Finding Inconsistencies in Programs with Loops

Temesghen Kahsai
Jorge A. Navas
Dejan Jovanovic
Martin Schäf

Abstract Inconsistent code is an important class of program abnormalities that appears in real-world code bases and often reveals serious bugs. A piece of code is inconsistent if it is not part of any safely terminating execution. Existing approaches to inconsistent code detection scale to programs with millions of lines of code, and have lead to patches in applications like the web-server Tomcat or the Linux kernel. However, the ability of existing tools to detect inconsistencies is limited by gross over-approximation of looping control-flow. We present a novel approach to inconsistent code detection that can reason about programs with loops without compromising precision. To that end, by leveraging recent advances in software model checking and Horn clause solving, we demonstrate how to encode the problem as a sequence of Horn clauses queries enabling us to detect inconsistencies that were previously unattainable.

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LPAR Conference 2015 Conference Paper

Gamifying Program Analysis

Daniel Fava
Julien Signoles
Matthieu Lemerre
Martin Schäf
Ashish Tiwari 0001

Abstract Abstract interpretation is a powerful tool in program verification. Several commercial or industrial scale implementations of abstract interpretation have demonstrated that this approach can verify safety properties of real-world code. However, using abstract interpretation tools is not always simple. If no user-provided hints are available, the abstract interpretation engine may lose precision during widening and produce an overwhelming number of false alarms. However, manually providing these hints is time consuming and often frustrating when re-running the analysis takes a lot of time. We present an algorithm for program verification that combines abstract interpretation, symbolic execution and crowdsourcing. If verification fails, our procedure suggests likely invariants, or program patches, that provide helpful information to the verification engineer and makes it easier to find the correct specification. By complementing machine learning with well-designed games, we enable program analysis to incorporate human insights that help improve their scalability and usability.

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