Scientific Programming

Scientific Programming Approaches to Deep Learning for Source Code Transformation


Publishing date
01 Jun 2021
Status
Closed
Submission deadline
15 Jan 2021

Lead Editor

1China University of Petroleum, Beijing, China

2North China University of Technology, Beijing, China

3Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, Pakistan

4Universiti Teknologi Malaysia, Johor Bahru, Malaysia

5CECOS University of IT and Emerging Sciences, Peshawar, Pakistan

This issue is now closed for submissions.
More articles will be published in the near future.

Scientific Programming Approaches to Deep Learning for Source Code Transformation

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Deep learning (DL) algorithms have many applications in a variety of fields. They are very valuable for a number of problem areas, for example, where little knowledge is available for experts to develop effective algorithms, where programs must adapt to changing conditions, or areas where there are large databases containing valuable implicit regularities to be discovered. Fortunately, the field of software engineering (SE) turns out to be a suitable domain where various software development, testing, and maintenance tasks can be framed as learning problems and can be solved in terms of DL algorithms.

Over the last few years, deep learning has emerged as an effective way of addressing various challenges in the field of source code-based intelligent SE. In intelligent SE, artificial intelligence (AI) techniques (such as deep learning) have been frequently applied to build intelligent tools from software artifacts (for example, source code, code commits, requirement documentation, bug reports, and execution logs) to improve software development and testing processes. Nowadays, there is a growing demand for the convergence of deep learning and software engineering to tackle issues in both software development and testing, such as the use of deep learning on source code to automate or semi-automate several non-trivial tasks, such as code search, code completion, code comments, code smell, generation of commit messages, bug localisation and fixing, clone detection, defect prediction, method names, and API templates learning. Online software development repositories and programming question and answer sites are visited by millions of users, and various manual activities of software developers and testers can be automated or semi-automated using data from these repositories.

This Special Issue welcomes submission of high-quality original articles that present novel and innovative ideas focusing on significant research efforts and new perspectives that explore state-of-the-art, source code-based SE methods and techniques, driven by the convergence of deep learning in different software development activities, with particular focus on the scientific application of algorithms, approaches, methodologies, and tools to source code that enable the effective, secure, and sustainable development of a complex software system. This Special Issue will provide an opportunity for researchers and professionals to explore and develop knowledge and insights into machine assisted source code-based automation across requirement engineering and testing lifecycle. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

  • Use of deep learning techniques and algorithms on source code for test automation, test coverage, verification, code checking, and quality assurance.
  • Code reviews, code clone detection, code comments, code completion, code search, and code smell prediction and identification based on deep code and inconsistency analysis using deep learning
  • New tools, approaches, frameworks, and models proposed for source code transformation and features extraction based on code analysis
  • Usage of deep learning in automated program repair, software testing, bug retrieval, bug-fixing, fault localisation, faults and defects predictability, detecting software weakness, and bug-specific named entity recognition
  • Software traceability, issue-commit link recovery, requirements classification, software size estimation, software effort estimation, software reliability model selection, and software maintainability using deep learning techniques
  • Deep learning on software changes, software repositories, and software community question-answering sites
  • Surveys, empirical studies, and systematic literature reviews in connection to deep learning on source for SE

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 5595850
  • - Research Article

Frame Duplication Forgery Detection and Localization Algorithm Based on the Improved Levenshtein Distance

Honge Ren | Walid Atwa | ... | Mahmoud Emam
  • Special Issue
  • - Volume 2021
  • - Article ID 6616564
  • - Research Article

An Intelligent Analytics Approach to Minimize Complexity in Ambiguous Software Requirements

Fariha Ashfaq | Imran Sarwar Bajwa | ... | Muhammad Ilyas
  • Special Issue
  • - Volume 2021
  • - Article ID 6624397
  • - Review Article

Analyzing the Classification Techniques for Bulk of Cursive Languages Data: An Overview

Mu Hong | Shah Nazir | ... | Wang Guan
  • Special Issue
  • - Volume 2021
  • - Article ID 5547766
  • - Research Article

Software Birthmark Usability for Source Code Transformation Using Machine Learning Algorithms

Keqing Guan | Shah Nazir | ... | Sadaqat ur Rehman
  • Special Issue
  • - Volume 2021
  • - Article ID 6613579
  • - Research Article

Key Performance Indicators for the Integration of the Service-Oriented Architecture and Scrum Process Model for IOT

Mengze Zheng | Islam Zada | ... | Amjad Ali
  • Special Issue
  • - Volume 2021
  • - Article ID 6691010
  • - Research Article

Applying Code Transform Model to Newly Generated Program for Improving Execution Performance

Bao Rong Chang | Hsiu-Fen Tsai | Po-Wen Su
  • Special Issue
  • - Volume 2021
  • - Article ID 6611407
  • - Research Article

Analysis of Service-Oriented Architecture and Scrum Software Development Approach for IIoT

Yanqing Cui | Islam Zada | ... | Muhammad Asshad
  • Special Issue
  • - Volume 2021
  • - Article ID 6661272
  • - Review Article

A Review on Multicriteria Decision Support System and Industrial Internet of Things for Source Code Transformation

Qinxia Hao | Shah Nazir | ... | Muhammad Ilyas
  • Special Issue
  • - Volume 2020
  • - Article ID 6647819
  • - Research Article

Software Piracy Awareness, Policy, and User Perspective in Educational Institutions

Zitian Liao | Shah Nazir | ... | Muhammad Shafiq
Scientific Programming
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