scholarly journals Evolving better RNAfold C source code

2017 ◽  
Author(s):  
W. B. Langdon
Keyword(s):  
Genetic Programming ◽  
Genetic Improvement ◽  
State Of The Art ◽  
Source Code ◽  
Secondary Structures ◽  
Base Sequence ◽  
Rna Molecules ◽  
Art Program ◽  
Code Changes ◽  
Source Code Changes

AbstractGrow and graft genetic programming (GGGP) can automatically evolve an existing state-of-the art program to give more accurate predictions of the secondary structures adapted by RNA molecules using their base sequence alone. That is, genetic improvement (GI) can make functional as well as non-functional source code changes.

scholarly journals Commit Message Generation for Source Code Changes

2019 ◽  
Author(s):  
Shengbin Xu ◽  
Yuan Yao ◽  
Feng Xu ◽  
Tianxiao Gu ◽  
Hanghang Tong ◽  
...  
Keyword(s):  
Software Evolution ◽  
State Of The Art ◽  
Source Code ◽  
Real Data ◽  
Program Comprehension ◽  
Sources Of Information ◽  
Structure Information ◽  
Code Structure ◽  
Code Changes ◽  
Source Code Changes

Commit messages, which summarize the source code changes in natural language, are essential for program comprehension and software evolution understanding. Unfortunately, due to the lack of direct motivation, commit messages are sometimes neglected by developers, making it necessary to automatically generate such messages. State-of-the-art adopts learning based approaches such as neural machine translation models for the commit message generation problem. However, they tend to ignore the code structure information and suffer from the out-of-vocabulary issue. In this paper, we propose CoDiSum to address the above two limitations. In particular, we first extract both code structure and code semantics from the source code changes, and then jointly model these two sources of information so as to better learn the representations of the code changes. Moreover, we augment the model with copying mechanism to further mitigate the out-of-vocabulary issue. Experimental evaluations on real data demonstrate that the proposed approach significantly outperforms the state-of-the-art in terms of accurately generating the commit messages.

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 RintC: fast and accuracy-aware decomposition of distributions of RNA secondary structures with extended logsumexp

2019 ◽  
Author(s):  
Hiroki Takizawa ◽  
Junichi Iwakiri ◽  
Kiyoshi Asai
Keyword(s):  
Computational Complexity ◽  
Probability Distribution ◽  
Numerical Computation ◽  
Source Code ◽  
Secondary Structures ◽  
Rna Molecules ◽  
The Stability ◽  
Decomposition Of Distributions ◽  
High Computational Complexity

The analysis of secondary structures is essential to understanding the function of RNAs. Because RNA molecules thermally fluctuate, it is necessary to analyze the probability distribution of secondary structures. Existing methods, however, are not applicable to long RNAs owing to their high computational complexity. Additionally, previous research has suffered from two numerical difficulties: overflow and significant numerical error. In this research, we reduced the computational complexity in calculating the landscape of the probability distribution of secondary structures by introducing a maximum-span constraint. In addition, we resolved numerical computation problems through two techniques: extended logsumexp and accuracy-guaranteed numerical computation. We analyzed the stability of the secondary structures of 16S ribosomal RNAs at various temperatures without overflow. The results obtained are consistent with in vivo assay results reported in previous research. Furthermore, we quantitatively assessed numerical stability using our method. These results demonstrate that the proposed method is applicable to long RNAs. Source code is available on https://github.com/eukaryo/rintc.

Sign in / Sign up

Export Citation Format

Share Document