An attribute selection process for software defect prediction

Khan, Jobaer Islam; Gias, Alim Ul; Siddik, Md. Saeed; Rahman, Md. Habibur; Khaled, Shah Mostafa; Shoyaib, Mohammad

doi:10.1109/iciev.2014.6850791

Cited by 5 publications

(9 citation statements)

References 19 publications

(27 reference statements)

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“…From our experience in this research, the problem of attributes selection was a key aspect to obtain satisfactory results, which was also confirmed by [16], [17], and [18]. With appropriate attributes identified, the better accuracy could be achieved for a smaller sets of attributes with a simple appropriate classifier.…”

Section: Resultsmentioning

confidence: 63%

A case study on machine learning model for code review expert system in software engineering

Madera¹,

Tomon²

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Code review is a key tool for quality assurance in software development. It is intended to find coding mistakes overlooked during development phase and lower risk of bugs in final product. In large and complex projects accurate code review is a challenging task. As code review depends on individual reviewer predisposition there is certain margin of source code changes that is not checked as it should. In this paper we propose machine learning approach for pointing project artifacts that are significantly at risk of failure. Planning and adjusting quality assurance (QA) activities could strongly benefit from accurate estimation of software areas endangered by defects. Extended code review could be directed there. The proposed approach has been evaluated for feasibility on large medical software project. Significant work was done to extract features from heterogeneous production data, leading to good predictive model. Our preliminary research results were considered worthy of implementation in the company where the research has been conducted, thus opening the opportunities for the continuation of the studies.

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Section: Resultsmentioning

confidence: 63%

A case study on machine learning model for code review expert system in software engineering

Madera¹,

Tomon²

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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“…Jobaer et al [5] provided a technique for choosing the best set of attributes in order to build a good prediction model. Authors utilized a cardinality to choose the best set of attributes.…”

Section: B Attribute Selectionmentioning

confidence: 99%

“…In recent years, many researches have been conducted on attribute selection [2], [3], [4], [5]. The existing attribute selection techniques can be grouped into two categories: featureranking and attribute-subset selection [6], [7].…”

Section: Introductionmentioning

confidence: 99%

SAL: An effective method for software defect prediction

Sharmin

Arefin

Abdullah-Al-Wadud

et al. 2015

2015 18th International Conference on Computer and Information Technology (ICCIT)

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For software quality assurance, software defect prediction (SDP) has drawn a great deal of attention in recent years. Its goal is to reduce verification cost, time and effort by predicting the defective modules efficiently. In SDP, proper attribute selection plays a significant role. However, selection of proper attributes and their representation in an efficient way are very challenging due to the lacking of standard set of attributes. To address these issues, we introduce Selection of Attribute with Log filtering (SAL) to select a proper set of attributes. Our proposed attribute selection process can effectively select the best set of attributes, which are relevant for the discrimination of defected and non-defected software modules. Further, we adopt log filtering to pre-process the input data. We have evaluated the proposed attribute selection method using several widely used publicly available datasets. The simulation results demonstrate that our method is more effective to improve the accuracy of SDP than the existing state-of-the-art methods.

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“…Feature selection (FS) methods are used to identify and retain the most relevant and informative data features for building accurate prediction models [1]- [5]. In literature, various terms such as attributes, metrics, and dimensions have been used for software features [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Feature Selection Methods in Software Defect Prediction Models

Ali,

Mazhar,

Shahzad

et al. 2023

IEEE Access

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Improving software quality by proactively detecting potential defects during development is a major goal of software engineering. Software defect prediction plays a central role in achieving this goal. The power of data analytics and machine learning allows us to focus our efforts where they are needed most. A key factor in the success of software fault prediction is selecting relevant features and reducing data dimensionality. Feature selection methods contribute by filtering out the most critical attributes from a plethora of potential features. These methods have the potential to significantly improve the accuracy and efficiency of fault prediction models. However, the field of feature selection in the context of software fault prediction is vast and constantly evolving, with a variety of techniques and tools available. Based on these considerations, our systematic literature review conducts a comprehensive investigation of feature selection methods used in the context of software fault prediction. The research uses a refined search strategy involving four reputable digital libraries, including IEEE Explore, Science Direct, ACM Digital Library, and Springer Link, to provide a comprehensive and exhaustive review through a rigorous analysis of 49 selected primary studies from 2014. The results highlight several important issues. First, there is a prevalence of filtering and hybrid feature selection methods. Second, single classifiers such as Naïve Bayes, Support Vector Machine, and Decision Tree, as well as ensemble classifiers such as Random Forest, Bagging, and AdaBoost are commonly used. Third, evaluation metrics such as area under the curve, accuracy, and F-measure are commonly used for performance evaluation. Finally, there is a clear preference for tools such as WEKA, MATLAB, and Python. By providing insights into current trends and practices in the field, this study offers valuable guidance to researchers and practitioners to make informed decisions to improve software fault prediction models and contribute to the overall improvement of software quality.

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An attribute selection process for software defect prediction

Cited by 5 publications

References 19 publications

A case study on machine learning model for code review expert system in software engineering

A case study on machine learning model for code review expert system in software engineering

SAL: An effective method for software defect prediction

Analysis of Feature Selection Methods in Software Defect Prediction Models

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