Revolution: Automatic Evolution of Mined Specifications

Mariani, Leonardo; Marchetto, Alessandro; Nguyen, Cu; Tonella, Paolo; Baars, Arthur I.

doi:10.1109/issre.2012.14

Cited by 18 publications

(16 citation statements)

References 12 publications

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“…When the traces include information about the state of the monitored application, it is possible to apply a style of inference that exploits state information rather than the sequences of the events. Some of the approaches that apply this strategy are ADABU [36], ReAjax [37], and Revolution [38]. These techniques can be extremely effective, but they can be applied only when suitable monitors that can efficiently inspect the state of the application can be implemented.…”

Section: Min−max Epsilon Propertymentioning

confidence: 99%

Timed k-Tail: Automatic Inference of Timed Automata

Pastore

Micucci

Mariani

2017

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)

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Abstract-Accurate and up-to-date models describing the behavior of software systems are seldom available in practice. To address this issue, software engineers may use specification mining techniques, which can automatically derive models that capture the behavior of the system under analysis.So far, most specification mining techniques focused on the functional behavior of the systems, with specific emphasis on models that represent the ordering of operations, such as temporal rules and finite state models. Although useful, these models are inherently partial. For instance, they miss the timing behavior, which is extremely relevant for many classes of systems and components, such as shared libraries and user-driven applications. Mining specifications that include both the functional and the timing aspects can improve the applicability of many testing and analysis solutions. This paper addresses this challenge by presenting the Timed k-Tail (TkT) specification mining technique that can mine timed automata from program traces. Since timed automata can effectively represent the interplay between the functional and the timing behavior of a system, TkT could be exploited in those contexts where time-related information is relevant.Our empirical evaluation shows that TkT can efficiently and effectively mine accurate models. The mined models have been used to identify executions with anomalous timing. The evaluation shows that most of the anomalous executions have been correctly identified while producing few false positives.

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Section: Min−max Epsilon Propertymentioning

confidence: 99%

Timed k-Tail: Automatic Inference of Timed Automata

Pastore

Micucci

Mariani

2017

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)

Self Cite

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“…Particularly, the event instances can be cached and checked against the new traces, new event instances can be mined from the new input traces, and the resulting set of event instances can be re-composed. This approach is not as fine-grained as in [30], but it can be extended to include some of the ideas in that work (e.g., by associating a timestamp with every transition in an event instance to support expiration).…”

Section: Invarimint Limitationsmentioning

confidence: 99%

Using Declarative Specification to Improve the Understanding, Extensibility, and Comparison of Model-Inference Algorithms

Beschastnikh

Brun

Abrahamson

et al. 2015

IIEEE Trans. Software Eng.

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Abstract-It is a staple development practice to log system behavior. Numerous powerful model-inference algorithms have been proposed to aid developers in log analysis and system understanding. Unfortunately, existing algorithms are typically declared procedurally, making them difficult to understand, extend, and compare. This paper presents InvariMint, an approach to specify modelinference algorithms declaratively. We applied the InvariMint declarative approach to two model-inference algorithms. The evaluation results illustrate that InvariMint (1) leads to new fundamental insights and better understanding of existing algorithms, (2) simplifies creation of new algorithms, including hybrids that combine or extend existing algorithms, and (3) makes it easy to compare and contrast previously published algorithms. InvariMint's declarative approach can outperform procedural implementations. For example, on a log of 50,000 events, InvariMint's declarative implementation of the kTails algorithm completes in 12 seconds, while a procedural implementation completes in 18 minutes. We also found that InvariMint's declarative version of the Synoptic algorithm can be over 170 times faster than the procedural implementation.

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“…Existing approaches on building finite state models [7,18,32,34,36] use similar state abstraction strategies as used in TLV. The STRAWBERRY tool [7] mines behavior protocols concerning usage of a web service.…”

Section: Related Workmentioning

confidence: 99%

“…KrKa et al [32] use DAIKON to generate a set of possible state invariants and then use SMT solvers to decide the feasibility of possible states (combinations of invariants) and transitions in the abstract model. Revolution [36] mines state based behavior model for systems whose behaviors may evolve with time.…”

Section: Related Workmentioning

confidence: 99%

TLV: abstraction through testing, learning, and validation

Sun

Xiao

Liu

et al. 2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

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A (Java) class provides a service to its clients (i.e., programs which use the class). The service must satisfy certain specifications. Different specifications might be expected at different levels of abstraction depending on the client's objective. In order to effectively contrast the class against its specifications, whether manually or automatically, one essential step is to automatically construct an abstraction of the given class at a proper level of abstraction. The abstraction should be correct (i.e., over-approximating) and accurate (i.e., with few spurious traces).We present an automatic approach, which combines testing, learning, and validation, to constructing an abstraction. Our approach is designed such that a large part of the abstraction is generated based on testing and learning so as to minimize the use of heavy-weight techniques like symbolic execution. The abstraction is generated through a process of abstraction/refinement, with no user input, and converges to a specific level of abstraction depending on the usage context. The generated abstraction is guaranteed to be correct and accurate. We have implemented the proposed approach in a toolkit named TLV and evaluated TLV with a number of benchmark programs as well as three real-world ones. The results show that TLV generates abstraction for program analysis and verification more efficiently.

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Revolution: Automatic Evolution of Mined Specifications

Cited by 18 publications

References 12 publications

Timed k-Tail: Automatic Inference of Timed Automata

Timed k-Tail: Automatic Inference of Timed Automata

Using Declarative Specification to Improve the Understanding, Extensibility, and Comparison of Model-Inference Algorithms

TLV: abstraction through testing, learning, and validation

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