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Method name recommendation based on source code metrics

JOURNAL OF COMPUTER LANGUAGES 2023年 74卷

作者： Parsa, Saeed Zakeri-Nasrabadi, Morteza Ekhtiarzadeh, Masoud Ramezani, Mohammad Iran Univ Sci & Technol Sch Comp Engn Tehran Iran Iran Univ Sci & Technol Sch Comp Engn Hengam StResalat Sq Tehran *** Iran

Method naming is a critical factor in program comprehension, affecting software quality. State-of-the-art naming techniques use deep learning to compute the methods' similarity considering their textual contents. They highly depend on identifiers' names and do not compute semantical interrelations among methods' instructions. source code metrics compute such semantical interrelations. This article proposes using source code metrics to measure semantical and structural cross-project similarities. The metrics constitute features of a KNN model, determining the k-most similar methods to a given method. Experiments with 4000000 Java methods on the proposed model demonstrate improvements in precision and recall of state-of-the-arts with 4.25 and 12.08%.

关键词： Method name recommendation code similarity source code metrics k-nearest neighbors Distributed representation

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SBFL fault localization considering fault-proneness

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JOURNAL OF SYSTEMS AND SOFTWARE 2025年 223卷

作者： Torkashvan, Reza Parsa, Saeed Branch, Babak Vaziri Islamic Azad Univ Dept Comp Engn Cent Tehran Branch Tehran Iran Iran Univ Sci & Technol Sch Comp Engn Tehran Iran

Fault localization is a critical phase in software debugging, often posing significant challenges and demanding extensive time for large and complex programs. Spectrum-based fault localization (SBFL) is a straightforward and cost-effective technique that leverages program execution logs to identify faulty statements. However, the effectiveness of SBFL can be compromised by biases in the test data set, which may not uniformly cover all code features. This study demonstrates that the integration of fault-proneness scores of program classes, predicted by a machine learning model utilizing source code metrics, with the fault-suspiciousness scores of program statements, estimated by SBFL, can enhance the accuracy and efficacy of fault localization. A Random Forest model is employed to predict the fault-proneness of classes in five Java projects from the Unified-BugDataset 1.2. Concurrently, three established SBFL formulas are used to compute the fault-suspiciousness of statements. Statements are ranked based on their faultiness scores, derived from a linear combination of class fault-proneness and statement fault-suspiciousness. This approach is compared with the original SBFL formulas using four evaluation metrics: F-measure, precision, recall, and accuracy. The results indicate that the proposed method surpasses the original SBFL formulas across all metrics and significantly reduces the search space for fault localization. These findings suggest that the integration of static and dynamic analysis provides amore reliable and efficient method for fault localization in software systems.

关键词： Fault localization Spectrum-based fault localization Fault-proneness Machine learning source code metrics Random Forest

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Dynamic creation of source code models for the extraction of code metrics data through grammar querying

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SCIENCE OF COMPUTER PROGRAMMING 2020年 196卷 102480-102480页

作者： Nunez-Varela, Alberto S. Perez-Gonzalez, Hector G. Martinez-Perez, Francisco E. Soubervielle-Montalvo, Carlos Perez-Cham, Oscar E. Univ Autonoma San Luis Potosi Fac Ingn Manuel Nava 8 San Luis Potosi 78216 San Luis Potosi Mexico

source code metrics extraction is a complex task that has to be done automatically given the current size of software. They are extracted using software metric tools and more generic extraction mechanisms. These mechanisms usually work by querying a source code representation model. These models are static, and the information that can be obtained from them is limited. In this work an extraction methodology is presented in which the model is created every time certain information is needed. This is accomplished by querying the language context-free grammar, and from the information obtained by the query, a dynamic model is created. Current extraction mechanisms work by querying a model, while the proposed methodology queries the grammar directly, thus the model is created afterwards from the query result, and contains all the needed information. A metrics tool is created based on the proposed methodology, and in order to prove the correct functioning of extracting the desired information from the source code, not as already predefined as in current tools, several metrics are extracted as defined by four existing metrics tools. Querying the language grammar allows access to all available data in the source code, regardless of the programming language and paradigm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： source code metrics metrics extraction Software metrics tool

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A Study on Low Complexity Models to Predict Flaws in the Linux source code 12

A Study on Low Complexity Models to Predict Flaws in the Lin...

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12th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Kanashiro, Lucas Ribeiro, Athos Silva, David Meirelles, Paulo Terceiro, Antonio Univ Sao Paulo FLOSS Competence Ctr Sao Paulo Brazil Univ Brasilia UnB Fac Gama Brasilia DF Brazil Linaro Ltd QA Serv Team Curitiba Parana Brazil

ISBN: (纸本)9781509050475

Due to the constant evolution of technology, each day brings new programming languages, development paradigms, and ways of evaluating processes. This is no different with source code metrics, where there is always new metric classes. To use a software metric to support decisions, it is necessary to understand how to perform the metric collection, calculation, interpretation, and analysis. The tasks of collecting and calculating source code metrics are most often automated, but how should we monitor them during the software development cycle? Our research aims to assist the software engineer to monitor metrics of vulnerability threats present in the source code through a reference prediction model, considering that real world software have non-functional security requirements, which implies the need to know how to monitor these requirements during the software development cycle. As a first result, this paper presents an empirical study on the evolution of the Linux project. Based on static analysis data, we propose low complexity models to study flaws in the Linux source code. About 391 versions of the project were analyzed by mining the official Linux repository using an approach that can be reproduced to perform similar studies. Our results show that it is possible to predict the number of warnings triggered by a static analyzer for a given software project revision as long as the software is continuously monitored.

关键词： source code Static Analysis source code metrics Common Weakness Enumeration Prediction Linux

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QualBoa: Reusability-aware Recommendations of source code Components 13

QualBoa: Reusability-aware Recommendations of Source Code Co...

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13th International Conference on Mining Software Repositories (MSR)

作者： Diamantopoulos, Themistoklis Thomopoulos, Klearchos Symeonidis, Andreas Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki Greece

ISBN: (纸本)9781450341868

Contemporary software development processes involve finding reusable software components from online repositories and integrating them to the source code, both to reduce development time and to ensure that the final software project is of high quality. Although several systems have been designed to automate this procedure by recommending components that cover the desired functionality, the reusability of these components is usually not assessed by these systems. In this work, we present QualBoa, a recommendation system for source code components that covers both the functional and the quality aspects of software component reuse. Upon retrieving components, QualBoa provides a ranking that involves not only functional matching to the query, but also a reusability score based on configurable thresholds of source code metrics. The evaluation of QualBoa indicates that it can be effective for recommending reusable source code.

关键词： Recommendation Systems in Software Engineering source code metrics code Reuse

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Application of LSSVM and SMOTE on Seven Open source Projects for Predicting Refactoring at Class Level 24

Application of LSSVM and SMOTE on Seven Open Source Projects...

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24th Asia-Pacific Software Engineering Conference (APSEC)

作者： Kumar, Lov Sureka, Ashish Thapar Univ Patiala Punjab India Ashoka Univ Rai Haryana India

ISBN: (纸本)9781538636817

source code refactoring consisting of modifying the structure of the source code without changing its functionality and external behavior. We present a method to predict refactoring candidates at class level which can help developers in improving their design and structure of source code while preserving the behavior. We propose a technique to predict refactoring candidates based on the application of a machine learning based framework. We use Least Squares Support Vector Machines (LS-SVM) as the learning algorithm, Principal Component Analysis (PCA) as a feature extraction technique and Synthetic Minority Over-sampling Technique (SMOTE) as a technique for handling imbalanced data. We start with 102 source code metrics as input features which are then reduced to 31 features after removing irrelevant and redundant features through statistical tests. We conduct a series of experiments on publicly available software engineering dataset consisting of seven open-source software systems in which the refactored classes are manually validated. We apply LS-SVM with three different functions: linear, polynomial and Radial Basis Function (RBF). Statistical significance test demonstrate that RBF kernel outperforms linear and polynomial kernel but there is no statistically significant difference between the performance of linear and polynomial kernel. Statistical significance test reveals that with-SMOTE technique outperforms without-SMOTE and all metrics outperforms PCA based metrics. The mean value of Area Under Curve (AUC) for LS-SVM RBF kernel is 0.96.

关键词： Empirical Software Engineering Machine Learning Predictive Modeling Refactoring Software Maintenance source code metrics

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Defects in The Next Release;Software Defect Prediction Based on source code Versions 26

Defects in The Next Release;Software Defect Prediction Based...

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26th Iranian Conference on Electrical Engineering (ICEE)

作者： Nehi, Molouk Mishmast Fakhrpoor, Zahra Moosavi, Mohammad R. Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn Shiraz Iran

ISBN: (纸本)9781538649169

Nowadays, automatic defect prediction has attracted wide attention, but many researchers neglected the availability of huge amount of data in version control systems. Deploying the next release of the project on time and on schedule urges software project managers to optimize efforts based on their limited human resources. Usually software projects have longterm version history from which we can build a repository of metrics, defects, fixes and causes. Having an accurate estimate of the distribution of bugs across components helps project managers to optimize the available resources by focusing on the problematic system parts. We have introduced a benchmark for interested readers in this field which provides all the data needed to apply a large array of prediction techniques proposed in the literature. Our dataset allows the reproduction of the experiments reported in this paper and their comparison with novel defect prediction approaches. We evaluated a selection of representative classification approaches and compared the performance of major classifiers using various evaluation measures such as Precision, Recall, F -measure, G -mean and AUC. The experimental results show that Random Forest is a powerful method and as a good candidate for software defect prediction can he used for further improvements.

关键词： version based defect prediction source code metrics benchmark dataset

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Faster issue resolution with higher technical quality of software

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SOFTWARE QUALITY JOURNAL 2012年第2期20卷 265-285页

作者： Bijlsma, Dennis Ferreira, Miguel Alexandre Luijten, Bart Visser, Joost Software Improvement Grp Amsterdam Netherlands Univ Amsterdam Amsterdam Netherlands Delft Univ Technol Delft Netherlands

We performed an empirical study of the relation between technical quality of software products and the issue resolution performance of their maintainers. In particular, we tested the hypothesis that ratings for source code maintainability, as employed by the Software Improvement Group (SIG) quality model, are correlated with ratings for issue resolution speed. We tested the hypothesis for issues of type defect and of type enhancement. This study revealed that all but one of the metrics of the SIG quality model show a significant positive correlation with the resolution speed of defects, enhancements, or both.

关键词： Software defects Defect resolution Maintainability source code metrics Rank correlation Issue tracker mining

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Empirical evaluation of software maintainability based on a manually validated refactoring dataset

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INFORMATION AND SOFTWARE TECHNOLOGY 2018年第Mar.期95卷 313-327页

作者： Hegedus, Peter Kadar, Istvan Ferenc, Rudolf Gyimothy, Tibor MTA SZTE Res Grp Artificial Intelligence Szeged Hungary Univ Szeged Szeged Hungary

Context: Refactoring is a technique for improving the internal structure of software systems. It has a solid theoretical background while being used in development practice also. However, we lack empirical research results on the real effect of code refactoring and its application. Objective: This paper presents a manually validated subset of a previously published dataset containing the refactorings extracted by the RefFinder tool, code metrics, and maintainability of 7 open-source systems. We found that RefFinder had around 27% overall average precision on the subject systems, thus our manually validated subset has substantial added value. Using the dataset, we studied several aspects of the refactored and non-refactored source code elements (classes and methods), like the differences in their maintainability and source code metrics. Method: We divided the source code elements into a group containing the refactored elements and a group with non-refactored elements. We analyzed the elements' characteristics in these groups using correlation analysis, Mann-Whitney U test and effect size measures. Results: source code elements subjected to refactorings had significantly lower maintainability than elements not affected by refactorings. Moreover, refactored elements had significantly higher size related metrics, complexity, and coupling. Also these metrics changed more significantly in the refactored elements. The results are mostly in line with our previous findings on the not validated dataset, with the difference that clone metrics had no strong connection with refactoring. Conclusions: Compared to the preliminary analysis using a not validated dataset, the manually validated dataset led to more significant results, which suggests that developers find targets for refactorings based on some internal quality properties of the source code, like their size, complexity or coupling, but not clone related metrics as reported in our previous studies. They do not just use th

关键词： code refactoring Manually validated empirical dataset source code metrics Software maintainability Empirical study

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An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes

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COMPUTER STANDARDS & INTERFACES 2017年 53卷 1-32页

作者： Kumar, Lov Misra, Sanjay Rath, Santanu Ku. Natl Inst Technol Dept CSE Rourkela India Atilim Univ Dept Comp Engn Ankara Turkey

Software fault prediction models are used to predict faulty modules at the very early stage of software development life cycle. Predicting fault proneness using source code metrics is an area that has attracted several researchers' attention. The performance of a model to assess fault proneness depends on the source code metrics which are considered as the input for the model. In this work, we have proposed a framework to validate the source code metrics and identify a suitable set of source code metrics with the aim to reduce irrelevant features and improve the performance of the fault prediction model. Initially, we applied a t-test analysis and univariate logistic regression analysis to each source code metric to evaluate their potential for predicting fault proneness. Next, we performed a correlation analysis and multivariate linear regression stepwise forward selection to find the right set of source code metrics for fault prediction. The obtained set of source code metrics are considered as the input to develop a fault prediction model using a neural network with five different training algorithms and three different ensemble methods. The effectiveness of the developed fault prediction models are evaluated using a proposed cost evaluation framework. We performed experiments on fifty six Open source Java projects. The experimental results reveal that the model developed by considering the selected set of source code metrics using the suggested source code metrics validation framework as the input achieves better results compared to all other metrics. The experimental results also demonstrate that the fault prediction model is best suitable for projects with faulty classes less than the threshold value depending on fault identification efficiency (low - 48.89%, median- 39.26%, and high - 27.86%).

关键词： Feature selection techniques Artificial neural network Ensemble method source code metrics Cost analysis framework

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