SAL: An effective method for software defect prediction

Sharmin, Sadia; Arefin, Rifat; Abdullah-Al-Wadud, M.; Nower, Naushin; Shoyaib, Mohammad

doi:10.1109/iccitechn.2015.7488065

Cited by 8 publications

(3 citation statements)

References 26 publications

(41 reference statements)

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“…One way is to create the vector from the hand crafted features. This approach assumes that an expert invents a set of features and selects best of them (e.g., [10,11]). Usually, these features include the statistical characteristics of code, such as its size, code complexity, code churn or process metrics.…”

Section: Rq1 What Techniques Have Been Applied To This Problem?mentioning

confidence: 99%

A Survey on Software Defect Prediction Using Deep Learning

et al. 2021

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Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect prediction. We analyse the recent works on the topic, study the methods for automatic learning of the semantic and structural features from the code, discuss the open problems and present the recent trends in the field.

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Section: Rq1 What Techniques Have Been Applied To This Problem?mentioning

confidence: 99%

A Survey on Software Defect Prediction Using Deep Learning

et al. 2021

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“…In [26], the authors addressed the problem of selection of attributes. The authors proposed a Log Filtering solution to select an attribute set.…”

Section: Related Workmentioning

confidence: 99%

Modeling Software Defects as Anomalies: A Case Study on Promise Repository

Neela¹,

Asif²,

Ami³

et al. 2017

JSW

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Software defect prediction is a highly studied domain in Software Engineering research due to its importance in software development. In literature, various classification methods with static code attributes have been used to predict defects. However, defected instances are very few compared to non-defected instances and as such lead to imbalanced data. Traditional machine learning techniques give poor results for such data. In this paper an anomaly detection technique for software defect prediction, is proposed which is not affected by imbalanced data. The technique incorporates both univariate and multivariate Gaussian distribution to model non-defected software module. The defected software modules are then predicted based on their deviation from the generated model. To evaluate our approach, we implemented the algorithm and tested it on the NASA datasets from the PROMISE repository. By utilizing this approach, we observed an average balance of 63.36% and 69.06% in univariate model and multivariate model respectively. Without utilizing optimization or filter, this approach yield better result than industry standard of 60%.

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“…create noise or be redundant. FS methods are used to remove such irrelevant and redundant features and can be divided into three broad categories namely Wrapper [14,18], Embedded [20], and Filter methods [13,15,16]. Among these, filter methods do not depend on a classifier to select a feature.…”

mentioning

confidence: 99%

Discretization and Feature Selection Based on Bias Corrected Mutual Information Considering High-Order Dependencies

Roy

Sharmin

Ali

et al. 2020

Advances in Knowledge Discovery and Data Mining

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Mutual Information (MI) based feature selection methods are popular due to their ability to capture the nonlinear relationship among variables. However, existing works rarely address the error (bias) that occurs due to the use of finite samples during the estimation of MI. To the best of our knowledge, none of the existing methods address the bias issue for the high-order interaction term which is essential for better approximation of joint MI. In this paper, we first calculate the amount of bias of this term. Moreover, to select features using χ 2 based search, we also show that this term follows χ 2 distribution. Based on these two theoretical results, we propose Discretization and feature Selection based on bias corrected Mutual information (DSbM). DSbM is extended by adding simultaneous forward selection and backward elimination (DSbM fb ). We demonstrate the superiority of DSbM over four state-of-the-art methods in terms of accuracy and the number of selected features on twenty benchmark datasets. Experimental results also demonstrate that DSbM outperforms the existing methods in terms of accuracy, Pareto Optimality and Friedman test. We also observe that compared to DSbM, in some dataset DSbM fb selects fewer features and increases accuracy.

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SAL: An effective method for software defect prediction

Cited by 8 publications

References 26 publications

A Survey on Software Defect Prediction Using Deep Learning

A Survey on Software Defect Prediction Using Deep Learning

Modeling Software Defects as Anomalies: A Case Study on Promise Repository

Discretization and Feature Selection Based on Bias Corrected Mutual Information Considering High-Order Dependencies

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