source file
Recently Published Documents


TOTAL DOCUMENTS

42
(FIVE YEARS 11)

H-INDEX

3
(FIVE YEARS 0)

2021 ◽  
Author(s):  
Thi Mai Anh Bui ◽  
Nhat Hai Nguyen

Precisely locating buggy files for a given bug report is a cumbersome and time-consuming task, particularly in a large-scale project with thousands of source files and bug reports. An efficient bug localization module is desirable to improve the productivity of the software maintenance phase. Many previous approaches rank source files according to their relevance to a given bug report based on simple lexical matching scores. However, the lexical mismatches between natural language expressions used to describe bug reports and technical terms of software source code might reduce the bug localization system’s accuracy. Incorporating domain knowledge through some features such as the semantic similarity, the fixing frequency of a source file, the code change history and similar bug reports is crucial to efficiently locating buggy files. In this paper, we propose a bug localization model, BugLocGA that leverages both lexical and semantic information as well as explores the relation between a bug report and a source file through some domain features. Given a bug report, we calculate the ranking score with every source files through a weighted sum of all features, where the weights are trained through a genetic algorithm with the aim of maximizing the performance of the bug localization model using two evaluation metrics: mean reciprocal rank (MRR) and mean average precision (MAP). The empirical results conducted on some widely-used open source software projects have showed that our model outperformed some state of the art approaches by effectively recommending relevant files where the bug should be fixed.


2021 ◽  
Vol 27 (S1) ◽  
pp. 1092-1094
Author(s):  
Benjamin Savitzky ◽  
Steven Zeltmann ◽  
Luis Rangel DaCosta ◽  
Peter Ercius ◽  
Mary Scott ◽  
...  

2021 ◽  
Vol 23 (07) ◽  
pp. 23-34
Author(s):  
Mrs. Vani Dave ◽  
◽  
Mr Sanjeev Kumar shukla ◽  

In this study, we propose a method to quickly search for similar source files for a given source file as a method to examine the origin of reused code. By outputting not only the same contents but also similar contents, it corresponds to the source file that has been changed during reuse. In addition, locality-sensitive hashing is used to search from a large number of source files, enabling fast search. By this method, it is possible to know the origin of the reused code. A case study was conducted on a library that is being reused written in C language. Some of the changes were unique to the project, and some were no longer consistent with the source files. As a result, it was possible to detect the source files that were reused from among the 200 projects with 92% accuracy. In addition, when we measured the execution time of the search using 4 files, the search was completed within 1 second for each file.


2021 ◽  
Vol 1 (193) ◽  
pp. 376-382
Author(s):  
Iryna Karamysheva ◽  
◽  
Roksolyana Nazarchuk ◽  
Kateryna Lishnievska ◽  
◽  
...  

The presented research focuses upon the analysis of additional specific tools (namely translation memory (TM) technologies) of SDL Trados Studio 2017 and MemoQ Translator Pro 2017 automated translation systems and their application for translation of English-language instructions into Ukrainian. With the help of the above-mentioned software tools 60 English-language operating instructions for household appliances have been translated into Ukrainian (three projects were created in both systems, each containing 10 instructions). TM is a database consisting of segments of source text (sentences, paragraphs, headings, etc.) and translations of each of these segments. TM, used in both SDL Trados Studio and MemoQ Translator Pro systems, significantly improves the quality, speed, consistency and efficiency of each translation task. SDL Trados Studio 2017 and MemoQ Translator Pro 2017 compare content of the current segment of the source file with segments of the same language already contained in the TM. If the system finds a similar segment that is currently stored in the TM, it prompts the translator to use a ready-made translation. The degree of equivalence between the segment of the source document and the segment contained in the TM is expressed as a percentage. Thus, both software tools capture the cases of «Exact match», «Perfect match» and «Fuzzy match». SDL Trados Studio 2017 andMemoQ Translator Pro 2017 slightly differ in segment statuses and colour segment marking. Both systems do not make adjustments automatically, but their identification and navigation capabilities allow one to quickly correct such errors by hand. Unfortunately, the initial focus on the Russian-language market (and, consequently, on the Russian language system) has led to another peculiarity of automated translation into Ukrainian in SDL Trados Studio and MemoQ Translator Pro systems, namely a large number of stylistic errors that require quality personalized correction.


Author(s):  
Liguo Yu

In C-alike programs, the source code is separated into header files and source files. During the software evolution process, both these two kinds of files need to adapt to changing requirement and changing environment. This paper studies the coevolution of header files and source files of C-alike programs. Using normalized compression distance that is derived from Kolmogorov complexity, we measure the header file difference and source file difference between versions of an evolving software product. Header files distance and source files distance are compared to understand their difference in pace of evolution. Mantel tests are performed to investigate the correlation of header file evolution and source file evolution. The study is performed on the source code of Apache HTTP web server.


Author(s):  
C. Maria Keet

Sharing, downloading, and reusing software is common-place, some of which is carried out legally with open source software. When it is not legal, it is unclear how many infringements have taken place: does an infringement count for the artefact as a whole or for each source file of a computer program? To answer this question, it must first be established whether a computer program should be considered as an integral whole, a collection, or a mere set of distinct files, and why. We argue that a program is a functional whole, availing of, and combining, arguments from mereology, granularity, modularity, unity, and function to substantiate the claim. The argumentation and answer contributes to the ontology of software artefacts, may assist industry in litigation cases, and demonstrates that the notion of unifying relation is operationalisable.


2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ming-Jui Chen ◽  
Hien-Thanh Le ◽  
Lanh-Thanh Le ◽  
Wei-Hsiung Tseng ◽  
Wei-Yang Lee ◽  
...  

To enhance driving safety, a counter beam light is proposed to meet CIE (International Commission on Illumination) specifications for tunnel lighting. The proposed new counter beam light (CBL) acts as a qualified counter beam light to help tunnel road lighting meet the CIE 88 : 2004 regulation standard in the threshold zone in both simulation and in practice. Through appropriate arrangements of the counter beam light and conventional fluorescent lights on the tunnel ceiling, we demonstrate that road tunnel lighting meeting CIE 88 : 2004 standards can be accomplished. Based on LiteStar four-dimensional simulation, the source file created through the measurement of the proposed CBL prototype achieved an average road surface brightness of 121 cd/m2, which is greater than the minimum regulation level of 105 cd/m2, a brightness uniformity of 0.88 (minimum regulation level of 0.4), longitudinal brightness uniformity of 0.98 (minimum regulation level of 0.6), a glare factor of 4.41% (maximum level of 15%), and a contrast revealing coefficient of 1.08, which is above the 0.6 minimum level in the threshold zone.


2020 ◽  
Author(s):  
Jaro Hokkanen ◽  
Jiri Kraus ◽  
Andreas Herten ◽  
Dirk Pleiter ◽  
Stefan Kollet

<p>  ParFlow is known as a numerical model that simulates the hydrologic cycle from the bedrock to the top of the plant canopy. The original codebase provides an embedded Domain-Specific Language (eDSL) for generic numerical implementations with support for supercomputer environments (distributed memory parallelism), on top of which the hydrologic numerical core has been built.<br>  In ParFlow, the newly developed optional GPU acceleration is built directly into the eDSL headers such that, ideally, parallelizing all loops in a single source file requires only a new header file. This is possible because the eDSL API is used for looping, allocating memory, and accessing data structures. The decision to embed GPU acceleration directly into the eDSL layer resulted in a highly productive and minimally invasive implementation.<br>  This eDSL implementation is based on C host language and the support for GPU acceleration is based on CUDA C++. CUDA C++ has been under intense development during the past years, and features such as Unified Memory and host-device lambdas were extensively leveraged in the ParFlow implementation in order to maximize productivity. Efficient intra- and inter-node data transfer between GPUs rests on a CUDA-aware MPI library and application side GPU-based data packing routines.<br>  The current, moderately optimized ParFlow GPU version runs a representative model up to 20 times faster on a node with 2 Intel Skylake processors and 4 NVIDIA V100 GPUs compared to the original version of ParFlow, where the GPUs are not used. The eDSL approach and ParFlow GPU implementation may serve as a blueprint to tackle the challenges of heterogeneous HPC hardware architectures on the path to exascale.</p>


The system identifies a duplicate record from the database using the machine learning method. We must pass unstructured data. Data are prepared using any natural language processing technique such as text similarity. This prepared data is then fed into the latest machine learning method called Random Forest. After this data collection, using these files, the target file is compared to the source file. We make input and output files. This is carried out until accurate efficiency is generated


Sign in / Sign up

Export Citation Format

Share Document