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Prediction of Source Code Quality Using Cyclomatic Complexity and Machine Learning
2020
International Journal of Advanced Trends in Computer Science and Engineering
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This paper analyses current tools that are being used for source code analysis and also proposes a method to use classification and cyclomatic complexity to predict the quality of the source code by giving ...
The automated analysis of source code quality can help the developers to decrease the potential source code anomalies without the help of a peer reviewer. ...
CONCLUSION Machine learning and cyclomatic complexity have proved to predict the quality of the source code precisely. ...
doi:10.30534/ijatcse/2020/34942020
fatcat:jyod643qdbey5enarmbomfyjga
Big Code: New Opportunities for Improving Software Construction
2016
Journal of Software
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detailed source-code information. ...
Big code is based on the idea that open source code repositories can be used to create new kind of programming tools and services to improve software reliability and construction. ...
This work has been funded by the European Union, through the European Regional Development Funds (ERDF); and the Principality of Asturias, through its Science, Technology and Innovation Plan (grant GRUPIN14 ...
doi:10.17706/jsw.11.11.1083-1088
fatcat:niutoovgzbgehhglypka4adfmm
Applying Unsupervised Machine Learning in Continuous Integration, Security and Deployment Pipeline Automation for Application Software System
2019
International journal of recent technology and engineering
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Unsupervised learning algorithms such as K-means Clustering, Symbolic Aggregate Approximation (SAX) and Markov are used for Quality and Performance Regression analysis in the CICD Model. ...
Applying machine learning helps in predicting the defects, failures and trends in the Continuous Integration Pipeline, whereas it can help in predicting the business impact in Continuous Delivery. ...
Jshint is a static code analysis tool used to check the code quality detecting syntax errors and potential problems in node.js scripts. ...
doi:10.35940/ijrte.d7387.118419
fatcat:ryoqxj3pqnfixjb5ehcnwwkxji
Detecting Security Fixes in Open-Source Repositories using Static Code Analyzers
[article]
2021
arXiv
pre-print
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In this paper, we study the extent to which the output of off-the-shelf static code analyzers can be used as a source of features to represent commits in Machine Learning (ML) applications. ...
The sources of reliable, code-level information about vulnerabilities that affect open-source software (OSS) are scarce, which hinders a broad adoption of advanced tools that provide code-level detection ...
Dario Di Nucci and Damian A. Tamburri are supported by the European Commission grants no. 825040 (RADON H2020) and no. 825480 (SODALITE H2020). ...
arXiv:2105.03346v1
fatcat:j7cyq4jom5cg3avnqwgz7g6ihy
Early WCET Prediction Using Machine Learning
2017
Worst-Case Execution Time Analysis
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We investigate the possibility of creating predictors of the WCET based on the C source code using machine-learning (work in progress). ...
However, a prediction -even coarse -of the future WCET would be helpful at design stages where only the source code is available. ...
We wish to thank Mathieu Serrurier for patiently sharing with us bits of its expertise in machine learning. ...
doi:10.4230/oasics.wcet.2017.5
dblp:conf/wcet/BonenfantCMS17
fatcat:iqk4b5h7efdjrerjnbsu7472zu
Using Code Coverage Metrics for Improving Software Defect Prediction
2018
Journal of Software
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First scenario resembles static analysis and acts as baseline model. Second scenario addresses coverage issues of the associated test cases for the source code. ...
Each scenario has been modeled and examined using thirteen different machine learning classifiers. Two rounds of experiments have been done. ...
Machine learning [35] is frequently encountered with two main bottlenecks: working with imbalanced data and selecting the best features for machine learning techniques. ...
doi:10.17706/jsw.13.12.654-674
fatcat:qquu6jdci5gqrapjobnwwqziou
Change Prediction through Coding Rules Violations
2017
Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering - EASE'17
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Static source code analysis is an increasingly important activity to manage software project quality, and is often found as a part of the development process. ...
CCS CONCEPTS • Software engineering → Software evolution.; Software maintenance tools.; KEYWORDS software quality, change prediction, issues, machine learning. ACM Reference format: ...
More recently, changes have been related with static code quality analysis, e.g., Romano et al. ...
doi:10.1145/3084226.3084282
dblp:conf/ease/TollinFZR17
fatcat:khacu2zadvcitihoxbhapiu73m
An integrated approach for power transformer modeling and manufacturing
2020
Procedia Manufacturing
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Essential characteristics of smart factories, such as flexibility and resource efficiency, can be leveraged and improved by the power of machine learning and optimization techniques. ...
As many of these constraints rely on forecasts, a learning system may provide the necessary predictions for these constraints. ...
Acknowledgements The research reported in this paper has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the ...
doi:10.1016/j.promfg.2020.02.076
fatcat:2wb6a6q6hbfdxcbmfwcmc6e3ey
Predicting Regression Test Failures Using Genetic Algorithm-Selected Dynamic Performance Analysis Metrics
[chapter]
2013
Lecture Notes in Computer Science
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Our framework is evaluated using a genetic algorithm for dynamic metric selection in combination with state-of-the-art machine learning classifiers. ...
We show that if a program is modified and some tests subsequently fail, then it is possible to predict with considerable accuracy which of the remaining tests will also fail which can be used to help prioritise ...
different training sets sizes, different prediction quality metrics, and different code section sizes, and evaluating the benefits of the GA metric selection feature with different machine learning algorithms ...
doi:10.1007/978-3-642-39742-4_13
fatcat:7sxkldajdreh7a7d7ektbr2u6e
Software quality: A Historical and Synthetic Content Analysis
[article]
2021
arXiv
pre-print
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engineering, advanced software testing, and improved defect and fault prediction with machine learning and data mining. ...
According to the analysis of the hot topics, it seems that future research will be directed into developing and using a full specter of new artificial intelligence tools (not just machine learning and ...
quality assurance with
testing, Software quality assurance
using verification, validation and
static analysis, Static and dynamic
analysis, to assure code quality,
Test case prioritisation in ...
arXiv:2106.14598v1
fatcat:kl4key2jyvfxbgcognavdv4mhu
A Survey on Machine Learning Techniques for Source Code Analysis
[article]
2021
arXiv
pre-print
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Objective: We aim to summarize the current knowledge in the area of applied machine learning for source code analysis. ...
Results: Our findings suggest that the usage of machine learning techniques for source code analysis tasks is consistently increasing. ...
identification, machine learning security source code analysis, machine learning software quality assessment, machine learning code summarization, machine learning program repair, machine learning code ...
arXiv:2110.09610v1
fatcat:jc6c3jnxcbekfbssyy7hn3zwxa
Detecting Design Patterns by Learning Embedded Code Features
2021
figshare.com
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One of many challenges facing machine learning techniques is embedding source code into representative vectors in the dimensional space. ...
This thesis aims to examine the code features and their contribution to predicting the design pattern used. ...
We discuss source code analysis and code features, machine learning and finally, data embedding. ...
doi:10.6084/m9.figshare.14686989.v1
fatcat:wykzqmsewnd5vo2phtms74etsy
Automated software vulnerability detection with machine learning
[article]
2018
arXiv
pre-print
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With the wealth of open source code available for analysis, there is an opportunity to learn the patterns of bugs that can lead to security vulnerabilities directly from data. ...
In this paper, we present a data-driven approach to vulnerability detection using machine learning, specifically applied to C and C++ programs. ...
Enxing, Nicolas Edwards, and Adam Zakaria for their heroic efforts creating the build and data ingestion pipeline that was used to generate the datasets for this work. ...
arXiv:1803.04497v2
fatcat:zki6t3xhhngprobr273vga334m
Using Data Mining for Static Code Analysis of C
[chapter]
2012
Lecture Notes in Computer Science
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Static analysis of source code is one way to find bugs and problems in large software projects. Many approaches to static analysis have been proposed. ...
Learning by example means trivial programmer adaptability (a problem with many other approaches), learning systems also has the advantage to be able to generalise and find problematic source code constructs ...
Related Work Both machine learning and static code analysis have been widely studied. ...
doi:10.1007/978-3-642-35527-1_50
fatcat:zmmomof7bvbvxlgv425t5dv7w4
AI Based False Positive Analysis of Software Vulnerabilities
2022
International Journal for Research in Applied Science and Engineering Technology
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In this review, we propose a Machine Learning grouping based programming shortcoming forecast approach for this difficult issue ...
Abstract: Programming measurements and shortcoming information having a place with a past programming variant are utilized to assemble the product issue expectation model for the following arrival of the ...
on and explore potential genuine up-sides first.
4) A Practical Approach for Ranking Software Warnings from Multiple Static Code Analysis Reports [4]-Static examination instruments inspect source code ...
doi:10.22214/ijraset.2022.42306
fatcat:vdhocro7pvhyzpgshefn6unmwe
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