An Effective Model to Predict the Extension of Code Changes in Bug Fixing Process Using Text Classifiers

2021 ◽  
Author(s):  
Reza Sepahvand ◽  
Reza Akbari ◽  
Sattar Hashemi ◽  
Omid Boushehrian
Keyword(s):  
Effective Model ◽  
Bug Fixing ◽  
Text Classifiers ◽  
Code Changes

scholarly journals Quick remedy commits and their impact on mining software repositories

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Fengcai Wen ◽  
Csaba Nagy ◽  
Michele Lanza ◽  
Gabriele Bavota
Keyword(s):  
Software Maintenance ◽  
Noisy Data ◽  
Mining Software Repositories ◽  
Software Repositories ◽  
Bug Fixing ◽  
Manual Analysis ◽  
Data Points ◽  
Software Maintenance And Evolution ◽  
Code Changes ◽  
Different Parts

AbstractMost changes during software maintenance and evolution are not atomic changes, but rather the result of several related changes affecting different parts of the code. It may happen that developers omit needed changes, thus leaving a task partially unfinished, introducing technical debt or injecting bugs. We present a study investigating “quick remedy commits” performed by developers to implement changes omitted in previous commits. With quick remedy commits we refer to commits that (i) quickly follow a commit performed by the same developer, and (ii) aim at remedying issues introduced as the result of code changes omitted in the previous commit (e.g., fix references to code components that have been broken as a consequence of a rename refactoring) or simply improve the previously committed change (e.g., improve the name of a newly introduced variable). Through a manual analysis of 500 quick remedy commits, we define a taxonomy categorizing the types of changes that developers tend to omit. The taxonomy can (i) guide the development of tools aimed at detecting omitted changes and (ii) help researchers in identifying corner cases that must be properly handled. For example, one of the categories in our taxonomy groups the reverted commits, meaning changes that are undone in a subsequent commit. We show that not accounting for such commits when mining software repositories can undermine one’s findings. In particular, our results show that considering completely reverted commits when mining software repositories accounts, on average, for 0.07 and 0.27 noisy data points when dealing with two typical MSR data collection tasks (i.e., bug-fixing commits identification and refactoring operations mining, respectively).

The Role of Visual Distractors in the Simon Effect

2017 ◽  
Vol 64 (6) ◽  
pp. 387-397 ◽  
Author(s):  
Luisa Lugli ◽  
Stefania D’Ascenzo ◽  
Roberto Nicoletti ◽  
Carlo Umiltà
Keyword(s):  
Simon Effect ◽  
Simon Task ◽  
Automatic Generation ◽  
Spatial Code ◽  
Attentional Model ◽  
Previous Evidence ◽  
Spatial Stimulus ◽  
Attentional Resources ◽  
Code Changes

Abstract. The Simon effect lies on the automatic generation of a stimulus spatial code, which, however, is not relevant for performing the task. Results typically show faster performance when stimulus and response locations correspond, rather than when they do not. Considering reaction time distributions, two types of Simon effect have been individuated, which are thought to depend on different mechanisms: visuomotor activation versus cognitive translation of spatial codes. The present study aimed to investigate whether the presence of a distractor, which affects the allocation of attentional resources and, thus, the time needed to generate the spatial code, changes the nature of the Simon effect. In four experiments, we manipulated the presence and the characteristics of the distractor. Findings extend previous evidence regarding the distinction between visuomotor activation and cognitive translation of spatial stimulus codes in a Simon task. They are discussed with reference to the attentional model of the Simon effect.

Getting Reimbursed: Testing Code Changes Are Here

2018 ◽  
Author(s):  
Antonio E. Puente ◽  
Neil H. Pliskin
Keyword(s):  
Code Changes

An Effective Model of the Overshooting Effect for Multiple-Input Gates in Nanometer Technologies

2014 ◽  
Vol E97.A (5) ◽  
pp. 1059-1074
Author(s):  
Li DING ◽  
Zhangcai HUANG ◽  
Atsushi KUROKAWA ◽  
Jing WANG ◽  
Yasuaki INOUE
Keyword(s):  
Effective Model ◽  
Multiple Input

Code changes reduce low-level congestion problems

2003 ◽  
Vol 1 (3) ◽  
pp. 32-36
Author(s):  
T. Daniels ◽  
J. Vanderlip
Keyword(s):  
Low Level ◽  
Code Changes ◽  
Congestion Problems
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