Automatic generation of test sequences form EFSM models using evolutionary algorithms

Automated test data generation through evolutionary testing (ET) is a topic of interest to the software engineering community. While there are many ET-based techniques for automatically generating test data from code, the problem of generating test data from an extended finite state machine (EFSMs) is more complex and has received little attention. In this paper, we introduce a novel approach that addresses the problem of generating input test sequences that trigger given feasible paths in an EFSM model by employing an ET-based technique. The proposed approach expresses the problem as a search for input parameters to be applied to a set of functions to be called sequentially. In order to apply ET-based technique, a new fitness function is introduced to cope with the case when a test target involves calls to a set of transitions sequentially. We evaluate our approach empirically using five sets of randomly generated paths through two EFSM case studies: INRES and class 2 transport protocols. In the experiments, we apply two search techniques: a random and an ET-based which utilizes our new fitness function. Experimental results show that the proposed approach produces input test sequences that trigger all the feasible paths used with a success rate of 100%, however, the random technique failed in most cases with a success rate of 20.8%

The AFLOW Fleet for Materials Discovery

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.Comment: 14 pages, 8 figure

Generating feasible transition paths for testing from an extended finite state machine (EFSM)

The problem of testing from an extended finite state machine (EFSM) can be expressed in terms of finding suitable paths through the EFSM and then deriving test data to follow the paths. A chosen path may be infeasible and so it is desirable to have methods that can direct the search for appropriate paths through the EFSM towards those that are likely to be feasible. However, generating feasible transition paths (FTPs) for model based testing is a challenging task and is an open research problem. This paper introduces a novel fitness metric that analyzes data flow dependence among the actions and conditions of the transitions in order to estimate the feasibility of a transition path. The proposed fitness metric is evaluated by being used in a genetic algorithm to guide the search for FTPs

End-to-End Attention-based Large Vocabulary Speech Recognition

Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov Models (HMMs). Most of these systems contain separate components that deal with the acoustic modelling, language modelling and sequence decoding. We investigate a more direct approach in which the HMM is replaced with a Recurrent Neural Network (RNN) that performs sequence prediction directly at the character level. Alignment between the input features and the desired character sequence is learned automatically by an attention mechanism built into the RNN. For each predicted character, the attention mechanism scans the input sequence and chooses relevant frames. We propose two methods to speed up this operation: limiting the scan to a subset of most promising frames and pooling over time the information contained in neighboring frames, thereby reducing source sequence length. Integrating an n-gram language model into the decoding process yields recognition accuracies similar to other HMM-free RNN-based approaches

Model-Based Adaptation of Software Communicating via FIFO Buffers

Software Adaptation is a non-intrusive solution for composing black-box components or services (peers) whose individual functionality is as required for the new system, but that present interface mismatch, which leads to deadlock or other undesirable behaviour when combined. Adaptation techniques aim at automatically generating new components called adapters. All the interactions among peers pass through the adapter, which acts as an orchestrator and makes the involved peers work correctly together by compensating for mismatch. Most of the existing solutions in this field assume that peers interact synchronously using rendezvous communication. However, many application areas rely on asynchronous communication models where peers interact exchanging messages via buffers. Generating adapters in this context becomes a difficult problem because peers may exhibit cyclic behaviour, and their composition often results in infinite systems. In this paper, we present a method for automatically generating adapters in asynchronous environments where peers interact using FIFO buffers.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Learning to Prove Theorems via Interacting with Proof Assistants

Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics. However, human experts have to construct proofs manually by entering tactics into the proof assistant. In this paper, we study the problem of using machine learning to automate the interaction with proof assistants. We construct CoqGym, a large-scale dataset and learning environment containing 71K human-written proofs from 123 projects developed with the Coq proof assistant. We develop ASTactic, a deep learning-based model that generates tactics as programs in the form of abstract syntax trees (ASTs). Experiments show that ASTactic trained on CoqGym can generate effective tactics and can be used to prove new theorems not previously provable by automated methods. Code is available at https://github.com/princeton-vl/CoqGym.Comment: Accepted to ICML 201