Automated Abstractions for Contract Validation

Caso, Guido de; Braberman, Vı́ctor; Garbervetsky, Diego; Uchitel, Sebastián

doi:10.1109/tse.2010.98

Cited by 30 publications

(42 citation statements)

References 26 publications

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“…4. Enhancements to an existing third-party technique [16], further corroborating our hypothesis that using inferred invariants during model construction yields higher-quality models. 5.…”

Section: Introductionsupporting

confidence: 74%

“…For example, an object-level invariant, such as size ≥ 0 holds at all program points. While developers can manually specify program invariants in the code or in other documentation [16,35], static and dynamic analyses can automatically infer invariants. Manually-specified invariants indicate desired behavior of a program, while automatically-inferred invariants identify actual behavior of the program.…”

Section: Invocation Traces and The K-tail Algorithmmentioning

confidence: 99%

“…Contractor [16] is the sole existing approach that creates FSM models from program invariants. Contractor uniquely characterizes the model's states by a combination of methods enabled in each state (this abstraction is thus referred to as enabledness abstraction).…”

Section: Program Invariantsmentioning

confidence: 99%

“…It has been observed previously that techniques that infer FSMs from observed executions suffer from inaccuracies [29,36,37]. On the other hand, with one notable exception [16], techniques that identify invariants capture restrictions on sequences of method invocations only implicitly. Our own evaluation corroborates this: Existing FSM-inference techniques had average precision above 90%, but average recall of only 34% on nine subject libraries we used.…”

Section: Introductionmentioning

confidence: 99%

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Automatic mining of specifications from invocation traces and method invariants

Krka

Brun

Medvidović

2014

Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

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Software library documentation often describes the APIs of individual methods, but does not capture the intended protocols and method interactions. This can cause developers to misuse the library by invoking its methods out of sequence, while also restricting effective runtime detection of protocol violations and automated verification of the client code. Specification mining, if accurate, can help mitigate these problems. There are two classes of specification mining techniques: one that observes library executions, and another that identifies internal-state invariants. Unfortunately, models inferred by these state-of-the-art techniques often suffer from inaccuracies. In this paper, we aim to improve the quality of mined specifications in order to facilitate API understanding, test generation, and verification tasks. To that end, we combine the two classes of techniques in two novel ways, producing a pair of new model-inference techniques. We find that our new techniques improve significantly upon the state-of-the-art when applied to nine off-the-shelf libraries, increasing the precision of the inferred models by 4% and the recall by 41% on average. Additionally, we find that our approach automatically exposes both behavior that avoids raising exceptions and good-practice rules that capture proper library use, while being robust to noisy inputs.

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“…4. Enhancements to an existing third-party technique [16], further corroborating our hypothesis that using inferred invariants during model construction yields higher-quality models. 5.…”

Section: Introductionsupporting

confidence: 74%

Section: Invocation Traces and The K-tail Algorithmmentioning

confidence: 99%

Section: Program Invariantsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic mining of specifications from invocation traces and method invariants

Krka

Brun

Medvidović

2014

Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

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“…The underlying idea has been introduced with the concept of Typestate in [13]. A similar abstraction has also been used in [5], which presents a technique to build an enabledness model from contracts and static analysis. Tautoko [4] generates similar models starting from an existing test suite.…”

Section: Related Workmentioning

confidence: 99%

Runtime Monitoring of Functional Component Changes with Behavior Models

Ghezzi

Mocci

Sangiorgio

2012

Models in Software Engineering

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Abstract. We consider the problem of run-time discovery and continuous monitoring of new components that live in an open environment. We focus on extracting a formal model-which may not be availableby observing the behavior of the running component. We show how the model built at run time can be enriched through new observations (dynamic model update). We also use the inferred model to perform runtime verification. That is, we try to identify if any changes are made to the component that modify its original behavior, contradict the previous observations, and invalidate the inferred model.

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Dynamic Specification Mining Based on Transformer

Gao

Wang

2022

Theoretical Aspects of Software Engineering

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Automated Abstractions for Contract Validation

Cited by 30 publications

References 26 publications

Automatic mining of specifications from invocation traces and method invariants

Automatic mining of specifications from invocation traces and method invariants

Runtime Monitoring of Functional Component Changes with Behavior Models

Dynamic Specification Mining Based on Transformer

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