Case Studies in Learning-Based Testing

Feng, Lei; Lundmark, Simon; Michaëlsson, Karl; Niu, Fei; Sindhu, Muddassar Azam; Wong, Peter Y. H.

doi:10.1007/978-3-642-41707-8_11

Cited by 25 publications

(30 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6. It describes that a document was opened (lines 1-10), an invalid option was entered (lines [11][12][13][14][15], and then the next input was the command to edit the document (lines [16][17][18][19][20][21][22][23]. Lines 24-33 represent the execution of option print, followed by a document save (lines [34][35][36][37][38][39][40][41][42].…”

Section: Information Gatheringmentioning

confidence: 99%

“…CIDs represent contexts and are mapped to states of the LKS (line 18), whereas actions are mapped to labels of the model. Whenever a CID is found in the context trace, it is checked whether it is the first context found so that variables initialState and previousState are set (lines [19][20]. If it is not the first context, it means that there has been a context found before and variable previousState contains its CID.…”

Section: Lks Constructionmentioning

confidence: 99%

“…Work on Learning-Based Testing (LBT) [47] [46] [20] combines model checking with inference algorithms, using a learned model to automatically derive test cases. Hence, the focus is on automating test case generation following an approach similar to CEGAR [11], but directed to testing, where counterexamples represent new test cases and queries to the inference algorithm.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Using contexts to extract models from code

2015

View full text Add to dashboard Cite

Behaviour models facilitate the understanding and analysis of software systems by providing an abstract view of their behaviours and also by enabling the use of validation and verification techniques to detect errors. However, depending on the size and complexity of these systems, constructing models may not be a trivial task, even for experienced developers. Model extraction techniques can automatically obtain models from existing code, thus reducing the effort and expertise required of engineers and helping avoid errors often present in manually constructed models. Existing approaches for model extraction often fail to produce faithful models, either because they only consider static information, which may include infeasible behaviours, or because they are based only on dynamic information, thus relying on observed executions, which usually results in incomplete models. This paper describes a model extraction approach based on the concept of contexts, which are abstractions of concrete states of a program, combining static and dynamic information. Contexts merge some of the advantages of using either type of information and, by their combination, can overcome some of their problems. The approach is partially implemented by a tool called LTS Extractor (LTSE), which translates information collected from execution traces produced by instrumented Java code to Labelled Transition Systems (LTS), which can be analysed in an existing verification tool. Results from case studies are presented and discussed, showing that, considering a level of abstraction and a set of execution traces, the produced models are correct descriptions of the programs from which they were extracted. Thus, they can be used for a variety of analyses, such as program understanding, validation, verification, and evolution.

show abstract

Section: Information Gatheringmentioning

confidence: 99%

Section: Lks Constructionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Using contexts to extract models from code

2015

View full text Add to dashboard Cite

show abstract

“…Active learning has, for instance, been successfully applied to learn models of (and to find mistakes in) implementations of protocols such as TCP [12] and TLS [8], to establish correctness of protocol implementations relative to a given reference implementation [2], and to generate models of a telephone switch [18] and a printer controller [21]. Learning-based testing [11] combines active learning and model checking. In this approach, which requires the presence of a formal specification of the system, model checking is used to guide the learning process.…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, which requires the presence of a formal specification of the system, model checking is used to guide the learning process. In [11] three industrial applications of learning-based testing are described from the web, automotive and finance domains.…”

Section: Introductionmentioning

confidence: 99%

Refactoring of Legacy Software Using Model Learning and Equivalence Checking: An Industrial Experience Report

Schuts

Hooman

Vaandrager

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Many companies struggle with large amounts of legacy software that is difficult to maintain and to extend. Refactoring legacy code typically requires large efforts and introduces serious risks because often crucial business assets are hidden in legacy components. We investigate the support of formal techniques for the rejuvenation of legacy embedded software, concentrating on control components. Model learning and equivalence checking are used to improve a new implementation of a legacy control component. Model learning is applied to both the old and the new implementation. The resulting models are compared using an equivalence check of a model checker. We report about our experiences with this approach at Philips. By gradually increasing the set of input stimuli, we obtained implementations of a power control service for which the learned behaviour is equivalent.

show abstract

Quantitative Safety Analysis of a Coordinated Emergency Brake Protocol for Vehicle Platoons

Bergenhem

Michaëlsson

Ström

2018

Leveraging Applications of Formal Methods, Verification and Validation. Distributed Systems

Self Cite

View full text Add to dashboard Cite

In this paper, we present a general methodology to estimate safety related parameter values of cooperative cyber-physical system-ofsystems. As a case study, we consider a vehicle platoon model equipped with a novel distributed protocol for coordinated emergency braking. The estimation methodology is based on learning-based testing; which is an approach to automated requirements testing that combines machine learning with model checking. Our methodology takes into account vehicle dynamics, control algorithm design, inter-vehicle communication protocols and environmental factors such as message packet loss rates. Empirical measurements from road testing of vehicle-to-vehicle communication in a platoon are modeled and used in our case study. We demonstrate that the minimum global time headway for our platoon model equipped with the CEBP function scales well with respect to platoon size.

show abstract

Case Studies in Learning-Based Testing

Cited by 25 publications

References 15 publications

Using contexts to extract models from code

Using contexts to extract models from code

Refactoring of Legacy Software Using Model Learning and Equivalence Checking: An Industrial Experience Report

Quantitative Safety Analysis of a Coordinated Emergency Brake Protocol for Vehicle Platoons

Contact Info

Product

Resources

About