An improved CNN model for within-project software defect prediction
C Pan, M Lu, B Xu, H Gao - Applied Sciences, 2019 - mdpi.com
To improve software reliability, software defect prediction is used to find software bugs and
prioritize testing efforts. Recently, some researchers introduced deep learning models, such …
prioritize testing efforts. Recently, some researchers introduced deep learning models, such …
Improving bug localization with word embedding and enhanced convolutional neural networks
Context: Automatic localization of buggy files can speed up the process of bug fixing to
improve the efficiency and productivity of software quality assurance teams. Useful semantic …
improve the efficiency and productivity of software quality assurance teams. Useful semantic …
Software fault localization: An overview of research, techniques, and tools
This chapter describes traditional and intuitive fault localization techniques, including
program logging, assertions, breakpoints, and profiling. Many advanced fault localization …
program logging, assertions, breakpoints, and profiling. Many advanced fault localization …
Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach
Robotic arm control has drawn a lot of attention along with the development of
industrialization. The methods based on myoelectric pattern recognition have been …
industrialization. The methods based on myoelectric pattern recognition have been …
Rlocator: Reinforcement learning for bug localization
Software developers spend a significant portion of time fixing bugs in their projects. To
streamline this process, bug localization approaches have been proposed to identify the …
streamline this process, bug localization approaches have been proposed to identify the …
[HTML][HTML] Best practices for evaluating IRFL approaches
Abstract Information retrieval fault localization (IRFL) is a popular research field and many
IRFL approaches have been proposed recently. Unfortunately, the evaluation of some of …
IRFL approaches have been proposed recently. Unfortunately, the evaluation of some of …
CodeGRU: Context-aware deep learning with gated recurrent unit for source code modeling
Context: Recently deep learning based Natural Language Processing (NLP) models have
shown great potential in the modeling of source code. However, a major limitation of these …
shown great potential in the modeling of source code. However, a major limitation of these …
Interpretation and machine translation towards google translate as a part of machine translation and teaching translation
VL Kane - Applied Translation, 2021 - appliedtranslation.lingcure.org
Abstract Language comprehension is the capacity of someone to properly understand the
language to fully communicate the message and details. When dialects are distinct, the …
language to fully communicate the message and details. When dialects are distinct, the …
How different is it between machine-generated and developer-provided patches?: An empirical study on the correct patches generated by automated program repair …
Background: Over the years, Automated Program Repair (APR) has attracted much attention
from both academia and industry since it can reduce the costs in fixing bugs. However, how …
from both academia and industry since it can reduce the costs in fixing bugs. However, how …
Graph-based seed object synthesis for search-based unit testing
Search-based software testing (SBST) generates tests using search algorithms guided by
measurements gauging how far a test case is away from exercising a coverage goal. The …
measurements gauging how far a test case is away from exercising a coverage goal. The …