Test Cases Selection Based on Source Code Features Extraction

Extracting valuable information from source code automatically was the subject of many research papers. Such information can be used for document traceability, concept or feature extraction, etc. In this paper, we used an Information Retrieval (IR) technique: Latent Semantic Indexing (LSI) for the automatic extraction of source code concepts for the purpose of test cases\u27 reduction. We used and updated the open source FLAT Eclipse add on to try several code stemming approaches. The goal is to check the best approach to extract code concepts that can improve the process of test cases\u27 selection or reduction

Towards a Framework for Generating Tests to Satisfy Complex Code Coverage in Java Pathfinder

We present work on a prototype tool based on the JavaPathfinder (JPF) model checker for automatically generating tests satisfying the MC/DC code coverage criterion. Using the Eclipse IDE, developers and testers can quickly instrument Java source code with JPF annotations covering all MC/DC coverage obligations, and JPF can then be used to automatically generate tests that satisfy these obligations. The prototype extension to JPF enables various tasks useful in automatic test generation to be performed, such as test suite reduction and execution of generated tests

TRACO: Source-to-Source Parallelizing Compiler

The paper presents a source-to-source compiler, TRACO, for automatic extraction of both coarse- and fine-grained parallelism available in C/C++ loops. Parallelization techniques implemented in TRACO are based on the transitive closure of a relation describing all the dependences in a loop. Coarse- and fine-grained parallelism is represented with synchronization-free slices (space partitions) and a legal loop statement instance schedule (time partitions), respectively. TRACO enables also applying scalar and array variable privatization as well as parallel reduction. On its output, TRACO produces compilable parallel OpenMP C/C++ and/or OpenACC C/C++ code. The effectiveness of TRACO, efficiency of parallel code produced by TRACO, and the time of parallel code production are evaluated by means of the NAS Parallel Benchmark and Polyhedral Benchmark suites. These features of TRACO are compared with closely related compilers such as ICC, Pluto, Par4All, and Cetus. Feature work is outlined

Additional illustrations of NL-SAR method for resolution-preserving (Pol)(In)SAR denoising

This document provides additional information and results of the method NL-SAR described in our paper: "NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising" submitted to IEEE Trans. on Geoscience and Remote Sensing [Deledalle et al., 2013]. NL-SAR is a fully automatic method for speckle reduction that handles amplitude, polarimetric and/or interferometric SAR data. It can process single look and multi-look images. The source code of the method is freely available at: http://www.math.u-bordeaux1.fr/cdeledal/nlsar.php

Метод генерации тестовых данных по исходному коду Java программ

Цель предлагаемого метода – повышение эффективности автоматической генерации и минимизации множества тестовых данных для обеспечения покрытия исходного кода Java программ. Рассматриваются различные виды покрытия, способы абстрактной интерпретации и редукции пространства поиска. В основу метода положены формальные методы анализа поведения модели.The objective of proposed method is to increase efficiency of automatic generation and minimization of test data set needed to guarantee coverage of source code of Java programs. Different kinds of coverage, methods of abstract interpretation and state-space reduction are discussed. The basis of the proposed method is formal methods of model behavior analysis

Improving the programming language translation process via static structure abstraction and algorithmic code transliteration

Fully automated programming language translation has been described as an unrealistic goal, with previous research being limited by a ceiling of 90% successful code translation. The key issues hindering automatic translation efficacy are the: maintainability of the translated constructs; full utilisation of the target language\u27s features; and amount of manual intervention required to complete the translation process. This study has concentrated on demonstrating improvements to the translation process by introducing the programming-language-independent, Unified Modelling Language (UML) and Computer Assisted Software Engineering (CASE) tools to the legacy-system language migration project. UML and CASE tools may be used to abstract the static framework of the source application to reduce the so called opaqueness of the translated constructs, yielding a significantly more maintainable product. The UML and CASE tools also enhance use of the target language features, through forward engineering of the native constructs of the target language during the reproduction of the static framework. Source application algorithmic code translation, performed as a separate process using transliteration, may preserve maximum functionality of the source application after completion of the static structure translation process. Introduction of the UML and CASE tools in conjunction with algorithmic code transliteration offers a reduction of the manual intervention required to complete the translation process

Implementation and benchmarking of the local weight window generation function for OpenMC

OpenMC is a community-driven open-source Monte Carlo neutron and photon transport simulation code. The Weight Window Mesh (WWM) function and an automatic Global Variance Reduction (GVR) method was recently developed and implemented in a developmental branch of OpenMC. This WWM function and GVR method broaden OpenMC\u27s usage in general purposes deep penetration shielding calculations. However, the Local Variance Reduction (LVR) method, which suits the source-detector problem, is still missing in OpenMC. In this work, the Weight Window Generator (WWG) function has been developed and benchmarked for the same branch. This WWG function allows OpenMC to generate the WWM for the source-detector problem on its own. Single-material cases with varying shielding and sources were used to benchmark the WWG function and investigate how to set up the particle histories utilized in WWG-run and WWM-run. Results show that there is a maximum improvement of WWM generated by WWG. Based on the above results, instructions on determining the particle histories utilized in WWG-run and WWM-run for optimal computation efficiency are given and tested with a few multi-material cases. These benchmarks demonstrate the ability of the OpenMC WWG function and the above instructions for the source-detector problem. This developmental branch will be released and merged into the main distribution in the future

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018