Learning to Reuse: Adaptive Model Learning for Evolving Systems

Damasceno, Carlos Diego Nascimento; Mousavi, Mohammad Reza; Simão, Adenilso da Silva

doi:10.1007/978-3-030-34968-4_8

Cited by 15 publications

(28 citation statements)

References 29 publications

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“…The output function is denoted by 𝜆 which maps each pair of a state and an input to an output. An FSM is deterministic if for each state and input, there exists at most one transition [8,20].…”

Section: Featured Behavioral Modelsmentioning

confidence: 99%

“…If the hypothesis is incorrect, a counterexample is provided in which the result of 𝐻 differs from the SUL language. Equivalence queries are used in the hypothesis validation phase of model learning [1,8,20].…”

Section: Model Learningmentioning

confidence: 99%

“…Active model learning is a process in which a model of software behavior is learned by presenting different inputs and observing the output [1,8,20]. Model learning can be used in the behavioral analysis of an SPL, for example in model-based testing and model checking [9].…”

Section: Introductionmentioning

confidence: 99%

“…When applied to variability-intensive systems, it is expected that the interactions with different variants or products will help to identify commonalities and variability. Adaptive model learning is a model learning approach in which previous models are reused to learn a new model, so that the speed of model learning can be increased [8]. In an SPL, the number of valid products may be exponential relative to the number of features [4,9].…”

Section: Introductionmentioning

confidence: 99%

“…Active model learning is performed using two types of queries, namely membership queries (MQ) and equivalence queries (EQ). The number of EQs and MQs and the length of these queries can affect the efficiency of model learning [1,8,20]. To evaluate the model learning methods in the context of SPLs, it is necessary to have a benchmark set that can clearly show how efficiently and accurately different learning methods perform.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Benchmark for Active Learning of Variability-Intensive Systems

Tavassoli¹,

Damasceno²,

Mousavi³

et al. 2022

Preprint

Self Cite

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Behavioral models are the key enablers for behavioral analysis of Software Product Lines (SPL), including testing and model checking. Active model learning comes to the rescue when family behavioral models are non-existent or outdated. A key challenge on active model learning is to detect commonalities and variability efficiently and combine them into concise family models. Benchmarks and their associated metrics will play a key role in shaping the research agenda in this promising field and provide an effective means for comparing and identifying relative strengths and weaknesses in the forthcoming techniques. In this challenge, we seek benchmarks to evaluate the efficiency (e.g., learning time and memory footprint) and effectiveness (e.g., conciseness and accuracy of family models) of active model learning methods in the software product line context. These benchmark sets must contain the structural and behavioral variability models of at least one SPL. Each SPL in a benchmark must contain products that requires more than one round of model learning with respect to the basic active learning 𝐿 * algorithm. Alternatively, tools supporting the synthesis of artificial benchmark models are also welcome.

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Section: Featured Behavioral Modelsmentioning

confidence: 99%

Section: Model Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Benchmark for Active Learning of Variability-Intensive Systems

Tavassoli¹,

Damasceno²,

Mousavi³

et al. 2022

Preprint

Self Cite

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Tree-Based Adaptive Model Learning

Ferreira

Heerdt

Silva

2022

Lecture Notes in Computer Science

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We extend the Kearns-Vazirani learning algorithm to be able to handle systems that change over time. We present a new learning algorithm that can reuse and update previously learned behavior, implement it in the LearnLib library, and evaluate it on large examples, to which we make small adjustments between two runs of the algorithm. In these experiments our algorithm significantly outperforms both the classic Kearns-Vazirani learning algorithm and the current state-of-the-art adaptive algorithm.⋆ Full implementation and experiment results available at https://github.com/UCL-PPLV/learnlib.

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Family-Based Fingerprint Analysis: A Position Paper

Damasceno

Strüber

2022

Lecture Notes in Computer Science

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Learning to Reuse: Adaptive Model Learning for Evolving Systems

Cited by 15 publications

References 29 publications

A Benchmark for Active Learning of Variability-Intensive Systems

A Benchmark for Active Learning of Variability-Intensive Systems

Tree-Based Adaptive Model Learning

Family-Based Fingerprint Analysis: A Position Paper

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