ALEX: Mixed-Mode Learning of Web Applications at Ease

Bainczyk, Alexander; Schieweck, Alexander; Isberner, Malte; Margaria, Tiziana; Neubauer, Johannes; Steffen, Bernhard

doi:10.1007/978-3-319-47169-3_51

Cited by 11 publications

(2 citation statements)

References 27 publications

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“…It was used in the development of tools like PSYCO [143,144], Tomte [145] and ALEX [146]. In whitebox scenarios (where source code is accessible), the Psyco and Sigma* [147] tools combine L* algorithm with symbolic execution to infer behavior models.…”

Section: Learnlib-based Developed Toolsmentioning

confidence: 99%

Model Learning: A Survey on Foundation, Tools and Applications

Ali,

Sun,

Zhao

2019

Preprint

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The quality and correct functioning of software components embedded in electronic systems are of utmost concern especially for safety and mission-critical systems. Model-based testing and formal verification techniques can be employed to enhance the reliability of software systems. Formal models form the basis and are prerequisite for the application of these techniques. An emerging and promising model learning technique can complement testing and verification techniques by providing learned models of black box systems fully automatically. This paper surveys one such state of the art technique called model learning which recently has attracted much attention of researchers especially from the domains of testing and verification. This survey paper reviews and provides comparison summaries highlighting the merits and shortcomings of learning techniques, algorithms, and tools which form the basis of model learning. This paper also surveys the successful applications of model learning technique in multidisciplinary fields making it promising for testing and verification of realistic systems.

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Section: Learnlib-based Developed Toolsmentioning

confidence: 99%

Model Learning: A Survey on Foundation, Tools and Applications

Ali,

Sun,

Zhao

2019

Preprint

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“…Model learning has been harnessed for black-box model checking (PELED; VARDI; YANNAKAKIS, 1999), detecting feature interactions (SHAHBAZ; PARREAUX; KLAY, 2007), analyzing network protocols (AARTS et al, 2012;FITERȂU-BROŞTEAN;HOWAR, 2017), and characterizing software evolution NIESE;STEFFEN, 2003;POLL, 2015). Furthermore, it has been used for testing web services (BAINCZYK et al, 2016), learning models from non-resettable systems (GROZ et al, 2015), supporting automated test generation (RAFFELT et al, 2009) and deriving failure models (CHAPMAN et al, 2015;KUNZE et al, 2016).…”

Section: Motivationmentioning

confidence: 99%

Learning finite state machine models of evolving systems: From evolution over time to variability in space

Damasceno¹

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Maintenance and evolution have been accepted as integral principles in the software development life-cycle. They are essential for any system that operates in or addresses problems or activities of the real world if it is to remain useful and profitable. Nevertheless, as time passes and modifications occur, modeling artifacts are often neglected due to the lack of proper maintenance. Hence, it may render outdated models and hinder the application of model-based reasoning techniques, such as model-based testing and model checking. To address these issues, recent academic and industrial studies have shown that finite state machine (FSM) model learning techniques are becoming increasingly popular in software verification and testing. Despite these advances, model learning algorithms are still hampered by scalability issues, as well as the constant changes over time that may require learning from scratch. Furthermore, there is a lack of investigations about learning strategies for software product lines (SPL), i.e., systems where variants shall co-exist to satisfying the needs of distinct market segments and, hence, incorporate variability in space. In this PhD Thesis, we improve upon the state-of-the-art of model-based software engineering by introducing theoretical and experimental contributions to address model learning in the setting of evolving systems that incorporate modifications over time and variability in space. Our main contributions are threefold: (i) We have introduced the partial-Dynamic L * M , an adaptive algorithm that explores models from pre-existing versions onthe-fly to discard redundant and deprecated knowledge in terms of input sequences that may not lead to state discovery. Using realistic models of the OpenSSL toolkit, we have shown that our algorithm has been more efficient than state-of-the-art techniques and less sensitive to software evolution. (ii) We have filled the gap of model learning algorithms for variability-intensive systems by introducing the FFSM Diff algorithm. It is an automated technique to identify similar behavior shared among product-specific FSMs, annotate states, and transitions with feature constraints, and integrate them into succinct featured finite state machines (FFSM). Using 105 FSMs derived from six SPLs of academic benchmarks, we have shown that our algorithm can effectively merge families of state machines into succinct FFSMs, especially if there is high feature reuse among products. (iii) We have extended our expertise upon the FFSM Diff algorithm and reported our experiences on learning FFSMs through product sampling. Our results have indicated that FFSMs learned by sampling can be as precise as those learned from exhaustive analysis and hence, collectively cover the behavior of an SPL.

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Track Introduction – Doctoral Symposium 2018

Lamprecht

2018

Leveraging Applications of Formal Methods, Verification and Validation. Verification

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ALEX: Mixed-Mode Learning of Web Applications at Ease

Cited by 11 publications

References 27 publications

Model Learning: A Survey on Foundation, Tools and Applications

Model Learning: A Survey on Foundation, Tools and Applications

Learning finite state machine models of evolving systems: From evolution over time to variability in space

Track Introduction – Doctoral Symposium 2018

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