scholarly journals Recommending Code Changes for Automatic Backporting of Linux Device Drivers

2016 ◽  
Author(s):  
Ferdian Thung ◽  
Xuan-Bach D. Le ◽  
David Lo ◽  
Julia Lawall
Keyword(s):  
Device Drivers ◽  
Code Changes

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

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

scholarly journals Guided pattern mining for API misuse detection by change-based code analysis

2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Sebastian Nielebock ◽  
Robert Heumüller ◽  
Kevin Michael Schott ◽  
Frank Ortmeier
Keyword(s):  
Pattern Mining ◽  
Third Party ◽  
Just In Time ◽  
False Alarms ◽  
Misuse Detection ◽  
Code Search ◽  
Usage Patterns ◽  
Development Processes ◽  
Code Changes ◽  
Api Usage

AbstractLack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or software crashes. Therefore, current research aims to automatically detect such misuses by comparing the way a developer used an API to previously inferred patterns of the correct API usage. While research has made significant progress, these techniques have not yet been adopted in practice. In part, this is due to the lack of a process capable of seamlessly integrating with software development processes. Particularly, existing approaches do not consider how to collect relevant source code samples from which to infer patterns. In fact, an inadequate collection can cause API usage pattern miners to infer irrelevant patterns which leads to false alarms instead of finding true API misuses. In this paper, we target this problem (a) by providing a method that increases the likelihood of finding relevant and true-positive patterns concerning a given set of code changes and agnostic to a concrete static, intra-procedural mining technique and (b) by introducing a concept for just-in-time API misuse detection which analyzes changes at the time of commit. Particularly, we introduce different, lightweight code search and filtering strategies and evaluate them on two real-world API misuse datasets to determine their usefulness in finding relevant intra-procedural API usage patterns. Our main results are (1) commit-based search with subsequent filtering effectively decreases the amount of code to be analyzed, (2) in particular method-level filtering is superior to file-level filtering, (3) project-internal and project-external code search find solutions for different types of misuses and thus are complementary, (4) incorporating prior knowledge of the misused API into the search has a negligible effect.

Understanding modern device drivers

2012 ◽  
Vol 47 (4) ◽  
pp. 87-98
Author(s):  
Asim Kadav ◽  
Michael M. Swift
Keyword(s):  
Device Drivers

Augmenting commit classification by using fine-grained source code changes and a pre-trained deep neural language model

2021 ◽  
Vol 135 ◽  
pp. 106566
Author(s):  
Lobna Ghadhab ◽  
Ilyes Jenhani ◽  
Mohamed Wiem Mkaouer ◽  
Montassar Ben Messaoud
Keyword(s):  
Language Model ◽  
Source Code ◽  
Fine Grained ◽  
Code Changes ◽  
Source Code Changes

scholarly journals A Mobile-Oriented System for Integrity Preserving in Audio Forensics

2019 ◽  
Vol 9 (15) ◽  
pp. 3097 ◽  
Author(s):  
Diego Renza ◽  
Jaime Andres Arango ◽  
Dora Maria Ballesteros
Keyword(s):  
Computational Cost ◽  
Digital Evidence ◽  
Audio Signals ◽  
Audio Forensics ◽  
Audio Recordings ◽  
Hash Code ◽  
Chain Of Custody ◽  
Code Changes ◽  
Hash Codes

This paper addresses a problem in the field of audio forensics. With the aim of providing a solution that helps Chain of Custody (CoC) processes, we propose an integrity verification system that includes capture (mobile based), hash code calculation and cloud storage. When the audio is recorded, a hash code is generated in situ by the capture module (an application), and it is sent immediately to the cloud. Later, the integrity of the audio recording given as evidence can be verified according to the information stored in the cloud. To validate the properties of the proposed scheme, we conducted several tests to evaluate if two different inputs could generate the same hash code (collision resistance), and to evaluate how much the hash code changes when small changes occur in the input (sensitivity analysis). According to the results, all selected audio signals provide different hash codes, and these values are very sensitive to small changes over the recorded audio. On the other hand, in terms of computational cost, less than 2 s per minute of recording are required to calculate the hash code. With the above results, our system is useful to verify the integrity of audio recordings that may be relied on as digital evidence.

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