Mining behavior models from enterprise web applications

Schur, Matthias; Röth, Andreas; Zeller, Andreas

doi:10.1145/2491411.2491426

Cited by 43 publications

(35 citation statements)

References 15 publications

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“…Such a specification should, for example, assert that communication libraries (e.g., Apache's [1] libraries) require a connection to be established before data is sent. Numerous techniques have been proposed to that end [3,4,10,15,19,20,32,33,41,46]. These techniques either (1) infer finite state machine (FSM) models that match the observed executions [3,4,9,20,32,38,44], or (2) identify the declarative class and method invariants by considering a library's state (i.e., its internal variables) [10,19,45].…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…Creating such a model that is optimally concise is NP-complete [4,23]. Like prior work [10,11,35,43], Perfume finds an approximate solution. To satisfy Challenge 3 from Section 2.1, this refinement must be efficient.…”

Section: Refinementmentioning

confidence: 99%

“…This paper focuses on helping developers understand how a system behaves and how it utilizes resources. Dynamic behavioral specification mining, e.g., [10,11,19,22,28,29,35,43] tackles the problem of inferring a behavioral model that summarizes a set of observed executions in a concise form. Such models have been used to improve developers' understanding of systems from logs [10], to generate test-cases [16] and test oracles [37], and to help make sense of complex distributed systems [9,30].…”

Section: Introductionmentioning

confidence: 99%

Behavioral resource-aware model inference

Ohmann

Herzberg

Fiss

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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Software bugs often arise because of differences between what developers think their system does and what the system actually does. These differences frustrate debugging and comprehension efforts. We describe Perfume, an automated approach for inferring behavioral, resource-aware models of software systems from logs of their executions. These finite state machine models ease understanding of system behavior and resource use.Perfume improves on the state of the art in model inference by differentiating behaviorally similar executions that differ in resource consumption. For example, Perfume separates otherwise identical requests that hit a cache from those that miss it, which can aid understanding how the cache affects system behavior and removing cache-related bugs. A small user study demonstrates that using Perfume is more effective than using logs and another model inference tool for system comprehension. A case study on the TCP protocol demonstrates that Perfume models can help understand non-trivial protocol behavior. Perfume models capture key system properties and improve system comprehension, while being reasonably robust to noise likely to occur in real-world executions.

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“…Creating such a model that is optimally concise is NP-hard [6]. Like prior work [3,4,16,18], Perfume finds an approximate solution.…”

Section: Refinementmentioning

confidence: 99%

“…Dynamic behavioral specification mining, e.g., [3,4,16,18], tackles this problem by inferring behavioral models that summarize all observed executions in a concise form. Such models have been used to improve developers' understanding of system implementations and to find, diagnose, and remove bugs [3].…”

Section: Introductionmentioning

confidence: 99%

Mining precise performance-aware behavioral models from existing instrumentation

Ohmann

Thai

Beschastnikh

et al. 2014

Companion Proceedings of the 36th International Conference on Software Engineering

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Software bugs often arise from differences between what developers envision their system does and what that system actually does. When faced with such conceptual inconsistencies, debugging can be very difficult. Inferring and presenting developers with accurate behavioral models of the system implementation can help developers reconcile their view of the system with reality and improve system quality.We present Perfume, a model-inference algorithm that improves on the state of the art by using performance information to differentiate otherwise similar-appearing executions and to remove false positives from the inferred models. Perfume uses a system's runtime execution logs to infer a concise, precise, and predictive finite state machine model that describes both observed executions and executions that have not been observed but that the system can likely generate. Perfume guides the model inference process by mining temporal performance-constrained properties from the logs, ensuring precision of the model's predictions. We describe the model inference process and demonstrate how it improves precision over the state of the art.

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Mining behavior models from enterprise web applications

Cited by 43 publications

References 15 publications

Automatic mining of specifications from invocation traces and method invariants

Automatic mining of specifications from invocation traces and method invariants

Behavioral resource-aware model inference

Mining precise performance-aware behavioral models from existing instrumentation

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