Model extraction and test generation from JUnit test suites

Seijas, Pablo Lamela; Thompson, Simon; Fernandez, Miguel Angel Francisco

doi:10.1145/2896921.2896927

Cited by 3 publications

(2 citation statements)

References 14 publications

(4 reference statements)

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“…In automaton learning [26,27], there are three basic types of algorithms: incremental learning algorithms, sequential learning algorithms and complete learning algorithms. In incremental learning, the system under learning (SUL) is learnt in a number of increments i = 1, 2, .…”

Section: Related Workmentioning

confidence: 99%

DLIQ: A Deterministic Finite Automaton Learning Algorithm through Inverse Queries

Haneef

Sindhu

2022

ITC

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Automaton learning has attained a renewed interest in many interesting areas of software engineering including formal verification, software testing and model inference. An automaton learning algorithm typically learns the regular language of a DFA with the help of queries. These queries are posed by the learner (Learning Algorithm) to a Minimally Adequate Teacher (MAT). The MAT can generally answer two types of queries asked by the learning algorithm; membership queries and equivalence queries. Learning algorithms can be categorized into two broad categories: incremental and complete learning algorithms. Likewise, these can be designed for 1-bit learning or k-bit learning. Existing automaton learning algorithms have polynomial (atleast cubic) time complexity in the presence of a MAT. Therefore, sometimes these algorithms even become fail to learn large complex software systems. In this research work, we have reduced the complexity of the Deterministic Finite Automaton (DFA) learning into lower bounds (from cubic to square form). For this, we introduce an efficient complete DFA learning algorithm through Inverse Queries (DLIQ) based on the concept of inverse queries introduced by John Hopcroft for state minimization of a DFA. The DLIQ algorithm takes O(|Ps||F|+|Σ|N) complexity in the presence of a MAT which is also equipped to answer inverse queries. We give a theoretical analysis of the proposed algorithm along with providing a proof correctness and termination of the DLIQ algorithm. We also compare the performance of DLIQ with ID algorithm by implementing an evaluation framework. Our results depict that DLIQ is more efficient than ID algorithm in terms of time complexity.

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Section: Related Workmentioning

confidence: 99%

DLIQ: A Deterministic Finite Automaton Learning Algorithm through Inverse Queries

Haneef

Sindhu

2022

ITC

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“…There are three main categories of algorithms proposed in literature for automaton learning [35], [36], including incremental learning algorithms, sequential learning algorithms and complete learning algorithms. In incremental learning, the system under learning (SUL) is learnt in a series of increments i = 1, 2, .…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Based Grammatical Inference Algorithm Using Block-Based Delta Inverse Strategy

Haneef

Sindhu

2023

IEEE Access

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A resurgent interest for grammatical inference aka automaton learning has emerged in several intriguing areas of computer sciences such as machine learning, software engineering, robotics and internet of things. An automaton learning algorithm commonly uses queries to learn the regular grammar of a Deterministic Finite Automaton (DFA). These queries are posed to a Minimum Adequate Teacher (MAT) by the learner (Learning Algorithm). The membership and equivalence queries which the learning algorithm may pose, are often capable of having their answers provided by the MAT. The three main categories of learning algorithms are incremental, sequential, and complete learning algorithms. In the presence of a MAT, the time complexity of existing DFA learning algorithms is polynomial. Therefore, in some applications these algorithms may fail to learn the system. In this study, we have reduced the time complexity of DFA learning from polynomial to logarithmic form. For this, we propose an efficient complete DFA learning algorithm; the Block based DFA Learning through Inverse Query (BDLIQ) using block based delta inverse strategy, which is based on the idea of inverse queries that John Hopcroft introduced for state minimization of a DFA. The BDLIQ algorithm possess O(| |N .logN ) complexity when a MAT is available. The MAT is also made capable of responding to inverse queries. We provide theoretical and empirical analysis of the proposed algorithm. Results show that our suggested approach for complete learning; BDLIQ algorithm, is more efficient than the ID algorithm in terms of time complexity.

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Overlap between Automated Unit and Acceptance Testing – a Systematic Literature Review

Breurkes

Gilson

Galster

2022

The International Conference on Evaluation and Assessment in Software Engineering 2022

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Model extraction and test generation from JUnit test suites

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 3 publications

References 14 publications

DLIQ: A Deterministic Finite Automaton Learning Algorithm through Inverse Queries

DLIQ: A Deterministic Finite Automaton Learning Algorithm through Inverse Queries

A Reinforcement Learning Based Grammatical Inference Algorithm Using Block-Based Delta Inverse Strategy

Overlap between Automated Unit and Acceptance Testing – a Systematic Literature Review

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