Dynamic Analysis for Diagnosing Integration Faults

Mariani, Leonardo; Pastore, Fabrizio; Pezzè, Mauro

doi:10.1109/tse.2010.93

Cited by 88 publications

(83 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some more powerful models with learning algorithms include: non-deterministic automata [60,29], probabilistic automata [19,17], Petri-nets [56], timed automata [57,35], I/O automata [5], and Büchi automata [39]. Despite their limited power, DFA and Mealy machine learning methods have recently been applied successfully to learn different types of complex systems such as web-services [15], X11 windowing programs [7], network protocols [24,9,22], and Java programs [59,26,46].…”

Section: Software Model Synthesismentioning

confidence: 99%

Improving active Mealy machine learning for protocol conformance testing

et al. 2013

View full text Add to dashboard Cite

Using a well-known industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model M R of reference implementation R, which serves as input for a model based testing tool that checks conformance of implementation I to M R . In addition, we also explore an alternative approach in which we learn a model M I of implementation I, which is compared to model M R using an equivalence checker. Our work uses a unique combination of software tools for model construction (Uppaal), active learning (LearnLib, Tomte), model-based testing (JTorX, TorXakis) and verification (CADP, MRMC). We show how these tools can be used for learning models of and revealing errors in implementations, present the new notion of a conformance oracle, and demonstrate how conformance oracles can be used to speed up conformance checking.

show abstract

Section: Software Model Synthesismentioning

confidence: 99%

Improving active Mealy machine learning for protocol conformance testing

et al. 2013

View full text Add to dashboard Cite

show abstract

“…The FSA could be part of the program specification or could be automatically inferred by monitoring test case execution, as it usually happens in anomaly detection techniques that compare passing and failing executions [3], [7].…”

Section: Ava: Automata Violations Analyzermentioning

confidence: 99%

“…For instance, fault localization techniques exploit the differences between the program spectra of failing and successful executions to localize likely faulty statements [1], [2]. Anomaly detection techniques identify the suspicious events that have been produced in a failed execution to capture the rationale of the problem under investigation [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

AVA: Supporting Debugging with Failure Interpretations

Pastore

Mariani

2013

2013 IEEE Sixth International Conference on Software Testing, Verification and Validation

Self Cite

View full text Add to dashboard Cite

Abstract-Several debugging techniques can be used to automatically identify the code fragments or the runtime events likely responsible of a failure. These techniques are useful, but can help reducing the debugging effort only to a given extent. In fact, even when these techniques are successful, software developers still have to invest a lot of effort in understanding if and why something detected as suspicious is really wrong.In this paper we present the tool implementing the AVA technique. AVA, compared to other approaches dedicated to automatic debugging, in addition to automatically identifying the events likely responsible of a failure, generates an explanation about why these events have been considered suspicious. This explanation can be used by developers to quickly discard imprecise outputs and more effectively work on the relevant anomalies.

show abstract

“…2 Assume that each of test cases that verifies different execution paths of the function B is given as (1,2) and (3,7), where the first field of each tuple is an input and the second is an output. The behavior of the function B integrated with the function C is represented by the two test cases.…”

Section: Reuse Mechanism Of Characterized Test Casesmentioning

confidence: 99%

“…And it is difficult to write significant test cases that verify interaction among the integrated source codes [1].…”

Section: Introductionmentioning

confidence: 99%

A Function Interaction Testing by Reusing Characterized Test Cases

Shin

Lee

2012

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThis letter proposes a reuse method of unit test cases, which characterize internal behaviors of a called function, for enhancing capability of automatic generation of test cases. Existing test case generation tools have limits in finding solutions to the deep call structure of the source code. In our approach, the complex call structure is simplified by reusing unit test cases of called functions. As unit test cases represent the characteristics of the called function, the internal behaviors of called functions are replaced by the test cases. This approach can be applicable to existing test tools for simplifying the process of generation and enhancing their capabilities.

show abstract

Dynamic Analysis for Diagnosing Integration Faults

Cited by 88 publications

References 47 publications

Improving active Mealy machine learning for protocol conformance testing

Improving active Mealy machine learning for protocol conformance testing

AVA: Supporting Debugging with Failure Interpretations

A Function Interaction Testing by Reusing Characterized Test Cases

Contact Info

Product

Resources

About