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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.

Software testing is an activity conducted to test the software under test. It has two approaches: manual testing and automation testing. Automation testing is an approach of software testing in which programming scripts are written to automate the process of testing. There are some software development projects under development phase for which automated testing is suitable to use and other requires manual testing. It depends on factors like project requirements nature, team which is working on the project, technology on which software is developing and intended audience that may influence the suitability of automated testing for certain software development project. In this paper we have developed machine learning model for prediction of automated testing adoption. We have used chi-square test for finding factors’ correlation and PART classifier for model development. Accuracy of our proposed model is 93.1624%.

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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