scholarly journals Aroma: code recommendation via structural code search

2019 ◽  
Vol 3 (OOPSLA) ◽  
pp. 1-28 ◽  
Author(s):  
Sifei Luan ◽  
Di Yang ◽  
Celeste Barnaby ◽  
Koushik Sen ◽  
Satish Chandra
Keyword(s):  
Code Search ◽  
Structural Code

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.

Improve Code Search via Reformulating Queries With Evolving Contexts

2021 ◽  
Author(s):  
P. Niranjan ◽  
Syed Abdul Moeed ◽  
V. Pranitha ◽  
T. Sam Spurgeon ◽  
V. Kavitha ◽  
...  
Keyword(s):  
Code Search

Tracelet-based code search in executables

2014 ◽  
Vol 49 (6) ◽  
pp. 349-360 ◽  
Author(s):  
Yaniv David ◽  
Eran Yahav
Keyword(s):  
Code Search

Improving Code Search with Co-Attentive Representation Learning

2020 ◽  
Author(s):  
Jianhang Shuai ◽  
Ling Xu ◽  
Chao Liu ◽  
Meng Yan ◽  
Xin Xia ◽  
...  
Keyword(s):  
Representation Learning ◽  
Code Search

A formalized optimum code search for q-ary trellis codes

2002 ◽  
Author(s):  
E.M. Rosa ◽  
W.C. Borelli ◽  
P.G. Farrell
Keyword(s):  
Trellis Codes ◽  
Code Search

Reordering Results of Keyword-Based Code Search for Supporting Simultaneous Code Changes

2015 ◽  
Author(s):  
Yusuke Sabi ◽  
Hiroaki Murakami ◽  
Yoshiki Higo ◽  
Shinji Kusumoto
Keyword(s):  
Code Search ◽  
Code Changes

Self-Attention Networks for Code Search

2021 ◽  
Vol 134 ◽  
pp. 106542
Author(s):  
Sen Fang ◽  
You-Shuai Tan ◽  
Tao Zhang ◽  
Yepang Liu
Keyword(s):  
Attention Networks ◽  
Code Search

Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning Approach for Semantic Code Search

2021 ◽  
Author(s):  
Lun Du ◽  
Xiaozhou Shi ◽  
Yanlin Wang ◽  
Ensheng Shi ◽  
Shi Han ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Learning Approach ◽  
Model Ensemble ◽  
Single Model ◽  
Semantic Code ◽  
Code Search ◽  
Semantic Code Search
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