Mistakes in binary conditions are a source of error in many software systems. They happen when developers use < or > instead of <= or >=. These boundary mistakes are hard to find for developers and pose a manual labor-intensive work. While researches have been proposing solutions to identify errors in boundary conditions, the problem remains a challenge. In this thesis, we propose deep learning models to learn mistakes in boundary conditions and train our model on approximately 1.6M examples with faults in different boundary conditions. We achieve an accuracy of 85.06%, a precision of 85.23% and a recall of 84.82% on a controlled dataset. Additionally, we perform tests on 41 real-world boundary condition bugs found from GitHub a...
Finding defects in proposed changes is one of the biggest motivations and expected outcomes of code ...
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors...
Automatically identifying struggling students learning to program can assist teachers in providing t...
Mistakes in binary conditions are a source of error in many software systems. They happen when devel...
Mistakes in boundary conditions are the cause of many bugs in software. These mistakes happen when, ...
Context: Automated software defect prediction (SDP) methods are increasingly applied, often with the...
The growing application of deep neural networks in safety-critical domains makes the analysis of fau...
Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing ac...
Background: Test resources are usually limited and therefore it is often not possible to completely ...
Deep learning frameworks play a key rule to bridge the gap between deep learning theory and practice...
Programmers spend a substantial amount of time manually repair- ing code that does not compile. We o...
Defect prediction is one of the key challenges in software development and programming language rese...
With the increasing adoption of Deep Learning (DL) for critical tasks, such as autonomous driving, t...
Abstract—Defect prediction is a very meaningful topic, par-ticularly at change-level. Change-level d...
Defect prediction models are proposed to help a team prioritize source code areas files that need So...
Finding defects in proposed changes is one of the biggest motivations and expected outcomes of code ...
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors...
Automatically identifying struggling students learning to program can assist teachers in providing t...
Mistakes in binary conditions are a source of error in many software systems. They happen when devel...
Mistakes in boundary conditions are the cause of many bugs in software. These mistakes happen when, ...
Context: Automated software defect prediction (SDP) methods are increasingly applied, often with the...
The growing application of deep neural networks in safety-critical domains makes the analysis of fau...
Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing ac...
Background: Test resources are usually limited and therefore it is often not possible to completely ...
Deep learning frameworks play a key rule to bridge the gap between deep learning theory and practice...
Programmers spend a substantial amount of time manually repair- ing code that does not compile. We o...
Defect prediction is one of the key challenges in software development and programming language rese...
With the increasing adoption of Deep Learning (DL) for critical tasks, such as autonomous driving, t...
Abstract—Defect prediction is a very meaningful topic, par-ticularly at change-level. Change-level d...
Defect prediction models are proposed to help a team prioritize source code areas files that need So...
Finding defects in proposed changes is one of the biggest motivations and expected outcomes of code ...
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors...
Automatically identifying struggling students learning to program can assist teachers in providing t...