Dynamically discovering likely program invariants to support program evolution

Ernst, F.; Cockrell,; Griswold,; Notkin,

doi:10.1109/icse.1999.841011

Cited by 109 publications

(171 citation statements)

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“…Program maintenance introduces errors [10,11] and many of these are due to violating invariants. When presented with Daikon's output, programmers are able to avoid breaking such invariants [12].…”

Section: Avoiding Bugsmentioning

confidence: 99%

The Daikon system for dynamic detection of likely invariants

Ernst

Perkins

Guo

et al. 2007

Science of Computer Programming

829

616

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Daikon is an implementation of dynamic detection of likely invariants; that is, the Daikon invariant detector reports likely program invariants. An invariant is a property that holds at a certain point or points in a program; these are often used in assert statements, documentation, and formal specifications. Examples include being constant (x = a), non-zero (x = 0), being in a range (a ≤ x ≤ b), linear relationships (y = ax + b), ordering (x ≤ y), functions from a library (x = fn(y)), containment (x ∈ y), sortedness (x is sorted), and many more. Users can extend Daikon to check for additional invariants.Dynamic invariant detection runs a program, observes the values that the program computes, and then reports properties that were true over the observed executions. Dynamic invariant detection is a machine learning technique that can be applied to arbitrary data. Daikon can detect invariants in C, C + +, Java, and Perl programs, and in record-structured data sources; it is easy to extend Daikon to other applications.Invariants can be useful in program understanding and a host of other applications. Daikon's output has been used for generating test cases, predicting incompatibilities in component integration, automating theorem proving, repairing inconsistent data structures, and checking the validity of data streams, among other tasks.Daikon is freely available in source and binary form, along with extensive documentation, at

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Section: Avoiding Bugsmentioning

confidence: 99%

The Daikon system for dynamic detection of likely invariants

Ernst

Perkins

Guo

et al. 2007

Science of Computer Programming

829

616

View full text Add to dashboard Cite

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“…In our previous work , we estimated the impact on the data by using dynamic invariants . To this end, we learned dynamic invariants from the original program by using DAIKON . Then, we generated checkers that check those invariants at runtime and run the mutations, and finally obtained a set of violated invariants for each mutation.…”

Section: Assessing Mutation Impactmentioning

confidence: 99%

Covering and Uncovering Equivalent Mutants

Schüler

Zeller

2012

Software Testing Verif & Rel

143

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SUMMARYMutation testing measures the adequacy of a test suite by seeding artificial defects (mutations) into a program. If a test suite fails to detect a mutation, it may also fail to detect real defects—and hence should be improved. However, there are also mutations that keep the program semantics unchanged and thus cannot be detected by any test suite. Such equivalent mutants must be weeded out manually, which is a tedious task. In this paper, we examine whether changes in coverage can be used to detect non‐equivalent mutants: If a mutant changes the coverage of a run, it is more likely to be non‐equivalent. In a sample of 140 manually classified mutations of seven Java programs with 5000 to 100 000 lines of code, we found that (i) the problem is serious and widespread—about 45% of all undetected mutants turned out to be equivalent; (ii) manual classification takes time—about 15 min per mutation; (iii) coverage is a simple, efficient and effective means to identify equivalent mutants—with a classification precision of 75% and a recall of 56%; and (iv) coverage as an equivalence detector is superior to the state of the art, in particular violations of dynamic invariants. Our detectors have been released as part of the open‐source JAVALANCHE framework; the data set is publicly available for replication and extension of experiments. Copyright © 2012 John Wiley & Sons, Ltd.

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“…A number of applications of machine learning techniques to verification problems have been proposed in the recent past. These include automatic synthesis of interface specifications for application programs [1], automatically learning the set of reachable states in regular model checking [20], black-box-testing [22] and its subsequent extension to adaptive model-checking [19] to learn an accurate finite state model of an unknown system starting from an approximate one, and learning likely program invariants based on observed values in sample executions [15].…”

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confidence: 99%

Automated Assume-Guarantee Reasoning for Simulation Conformance

Chaki¹,

Clarke²,

Sinha³

et al. 2005

Computer Aided Verification

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We address the issue of efficiently automating assume-guarantee reasoning for simulation conformance between finite state systems and specifications. We focus on a non-circular assume-guarantee proof rule, and show that there is a weakest assumption that can be represented canonically by a deterministic tree automata (DTA). We then present an algorithm L T that learns this DTA automatically in an incremental fashion, in time that is polynomial in the number of states in the equivalent minimal DTA. The algorithm assumes a teacher that can answer membership and candidate queries pertaining to the language of the unknown DTA. We show how the teacher can be implemented using a model checker. We have implemented this framework in the COMFORT toolkit and we report encouraging results (over an order of magnitude improvement in memory consumption) on non-trivial benchmarks. IntroductionFormal verification is an important tool in the hands of software practitioners for ascertaining correctness of safety critical software systems. However, scaling formal techniques like model checking [11] to concurrent software of industrial complexity remains an open challenge. The primary hurdle is the state-space explosion problem whereby the number of reachable states of a concurrent system increases exponentially with the number of components.Two paradigms hold the key to alleviating state-space explosion -abstraction [10,9] and compositional reasoning [23,8]. Both of these techniques have been extensively studied by the formal verification community and there have been significant breakthroughs from time to time. One of the most important advancements in the domain of compositional analysis is the concept of assume-guarantee [23] (AG) reasoning. The essential idea here is to model-check each component independently by making an assumption about its environment, and then discharge the assumption on the collection of the rest of the components. A variety of AG proof-rules are known, of which we will concern ourselves with the following non-circular rule called AG-NC:where M 1 M 2 is the concurrent system to be verified, S is the specification, and an appropriate notion of conformance between the system and the specification. AG-NC is known to be sound and complete for a number of conformance notions, including K.

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Dynamically discovering likely program invariants to support program evolution

Cited by 109 publications

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The Daikon system for dynamic detection of likely invariants

The Daikon system for dynamic detection of likely invariants

Covering and Uncovering Equivalent Mutants

Automated Assume-Guarantee Reasoning for Simulation Conformance

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