The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning

Isberner, Malte; Howar, Falk; Steffen, Bernhard

doi:10.1007/978-3-319-11164-3_26

Cited by 157 publications

(109 citation statements)

References 18 publications

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“…Unlike Konure, all of these techniques focus on characterizing specific aspects of program behavior and do not aspire to capture the complete behavior of the application. State Machine Model Learning: State machine learning algorithms [7,10,20,22,33,39,45,52,59,69,72] construct partial representations of program functionality in the form of finite automata with states and transition rules. State fuzzing tools [6,28,58] hypothesize state machines for programs.…”

Section: Related Workmentioning

confidence: 99%

Using active learning to synthesize models of applications that access databases

Shen

Rinard

2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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We present Konure, a new system that uses active learning to infer models of applications that access relational databases. Konure comprises a domain-specific language (each model is a program in this language) and associated inference algorithm that infers models of applications whose behavior can be expressed in this language. The inference algorithm generates inputs and database contents, runs the application, then observes the resulting database traffic and outputs to progressively refine its current model hypothesis. Because the technique works with only externally observable inputs, outputs, and database contents, it can infer the behavior of applications written in arbitrary languages using arbitrary coding styles (as long as the behavior of the application is expressible in the domain-specific language). Konure also implements a regenerator that produces a translated Python implementation of the application that systematically includes relevant security and error checks.

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Section: Related Workmentioning

confidence: 99%

Using active learning to synthesize models of applications that access databases

Shen

Rinard

2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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“…We used the TTT learning algorithm [30] for all experiments presented in the following as it performed best in our experiments. Furthermore, we used the random-walk equivalence oracle provided by LearnLib to perform equivalence queries.…”

Section: ) Application-specific Componentsmentioning

confidence: 99%

“…These high computation times for learning comparably simple models make apparent that there is a need to keep the number and length of queries to be executed as small as possible. This can, e.g., be achieved via domain-specific optimisations, heuristics and smart test selection [29], [42], or via algorithmic advantages [30].…”

Section: E Efficiencymentioning

confidence: 99%

Model-Based Testing IoT Communication via Active Automata Learning

Tappler

Aichernig

Bloem

2017

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

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This paper presents a learning-based approach to detecting failures in reactive systems. The technique is based on inferring models of multiple implementations of a common specification which are pair-wise cross-checked for equivalence. Any counterexample to equivalence is flagged as suspicious and has to be analysed manually. Hence, it is possible to find possible failures in a semi-automatic way without prior modelling.We show that the approach is effective by means of a case study. For this case study, we carried out experiments in which we learned models of five implementations of MQTT brokers/servers, a protocol used in the Internet of Things. Examining these models, we found several violations of the MQTT specification. All but one of the considered implementations showed faulty behaviour. In the analysis, we discuss effectiveness and also issues we faced. Implementations Abstract Models Single Abstract ModelSingle Abstract Model

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“…Related Work Mining temporal logic requirements is an emerging field of research in the analysis of cyber-physical systems (CPS) [2, 5, 7-9, 14, 16, 17, 20, 26, 27]. This approach is orthogonal to active automata learning (AAL) such as L * Angluin's algorithm [3] and its recent variants [15,25]. AAL is suitable to capture the behaviours of black-box reactive systems and it has been successfully employed in the field of CPS to learn how to interact with the surrounding environments [10,13].…”

Section: Introductionmentioning

confidence: 99%

A Robust Genetic Algorithm for Learning Temporal Specifications from Data

Nenzi

Silvetti

Bartocci

et al. 2018

Lecture Notes in Computer Science

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We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system. We assume the training set to be labeled by human experts and that we have access only to a limited amount of data, typically noisy. We provide a systematic approach to synthesize both the syntactical structure and the parameters of the temporal logic formula using a two-steps procedure: first, we leverage a novel evolutionary algorithm for learning the structure of the formula; second, we perform the parameter synthesis operating on the statistical emulation of the average robustness for a candidate formula w.r.t. its parameters. We compare our results with our previous work [9] and with a recently proposed decision-tree [8] based method. We present experimental results on two case studies: an anomalous trajectory detection problem of a naval surveillance system and the characterization of an Ineffective Respiratory effort, showing the usefulness of our work.

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The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning

Cited by 157 publications

References 18 publications

Using active learning to synthesize models of applications that access databases

Using active learning to synthesize models of applications that access databases

Model-Based Testing IoT Communication via Active Automata Learning

A Robust Genetic Algorithm for Learning Temporal Specifications from Data

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