Automatic mining of specifications from invocation traces and method invariants

Krka, Ivo; Brun, Yuriy; Medvidović, Nenad

doi:10.1145/2635868.2635890

Cited by 59 publications

(55 citation statements)

References 62 publications

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“…Combining state machine inference with data constraints Recently, Krka et al [30] Krka et al's idea of inferring a set of 'global' rules that pertain to the full set of program traces is very similar to what happens in our approach. We infer all of the data models at once, in advance of program inference.…”

Section: Related Workmentioning

confidence: 88%

“…These had been used previously for their experiments on FSM inference [30]. The traces were provided in the Daikon format.…”

Section: Trace Collectionmentioning

confidence: 99%

“…On the other hand, mutations could be triggered that changed the sequencing of events in such an obvious way that they failed to properly probe the ability of MINT to infer some of the more subtle data-related event sequences. Finally, there was the problem that this hampered our reuse of the traces from Krka et al's studies [30] (the three Java systems listed above). Although the traces were available, the exact inputs used to extract them (necessary to collect the mutated traces) were not.…”

Section: Synthesis Of Negative Tracesmentioning

confidence: 99%

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Inferring extended finite state machine models from software executions

2015

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The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering techniques focus either on control-specific behaviour (e.g., in the form of Finite State Machines), or data-specific behaviour (e.g., as pre / post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these techniques can be non-deterministic, the inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presentsWe thank Ivo Krka at the University of Southern California for providing us with some of the traces for the evaluation. Ramsay Taylor and John Derrick are supported by the EU FP7 PROWESS projects. Neil Walkinshaw is grateful for the support provided by the UK Ministry of Defence (MOD) through the BATS and HASTE projects. Information contained in this document should not be interpreted as representing the views of the MOD, nor should it be assumed that it reflects any current or future MOD policy. The information cannot supersede any statutory or contractual requirements or liabilities and is offered without prejudice or commitment.

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Section: Related Workmentioning

confidence: 88%

“…These had been used previously for their experiments on FSM inference [30]. The traces were provided in the Daikon format.…”

Section: Trace Collectionmentioning

confidence: 99%

Section: Synthesis Of Negative Tracesmentioning

confidence: 99%

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Inferring extended finite state machine models from software executions

2015

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“…To address this issue, execution trace analysis techniques are often used for some purposes, such as program comprehension [1] and specification mining [2]- [5]. For aiding in program comprehension, visualizing object interactions in execution traces as reverse-engineered sequence diagrams is a promising approach [1].…”

Section: Introductionmentioning

confidence: 99%

Identifying Core Objects for Trace Summarization by Analyzing Reference Relations and Dynamic Properties

Noda

Kobayashi

Atsumi

2018

IEICE Trans. Inf. & Syst.

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Kunihiro NODA†a) , Nonmember, Takashi KOBAYASHI †b) , and Noritoshi ATSUMI † †c) , Members SUMMARY Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often afflicted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactified reverseengineered sequence diagram that contains system's key behaviors. To identify core objects, first, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more effective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-theart trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a significant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.

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“…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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Automatic mining of specifications from invocation traces and method invariants

Cited by 59 publications

References 62 publications

Inferring extended finite state machine models from software executions

Inferring extended finite state machine models from software executions

Identifying Core Objects for Trace Summarization by Analyzing Reference Relations and Dynamic Properties

Behavioral resource-aware model inference

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