Classification with reject option for software defect prediction

Mesquita, Diego; Rocha, Lincoln S.; Gomes, Joo Paulo P.; Neto, Ajalmar R. Rocha

doi:10.1016/j.asoc.2016.06.023

Cited by 35 publications

(13 citation statements)

References 32 publications

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“…These comprehend both contributions to Machine Learning and applications in other areas. In partnership with software engineering researchers, we developed a reject-option framework for Software Defect Prediction [Mesquita et al 2016a]. We proposed the use of the Successive Projections Algorithm to prune Extreme Learning Machines [Mesquita et al 2015a].…”

Section: Summary Of Publicationsmentioning

confidence: 99%

Machine Learning for Incomplete Data

Mesquita¹,

Gomes²

2018

Anais Do Concurso De Teses E Dissertações Da SBC (CTD-SBC)

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Methods based on basis functions and similarity measures are widely used in machine learning and related fields. These methods often take for granted that data is fully observed and are not equipped to handle incomplete data in an organic manner. This assumption is often flawed, as incomplete data is a fact in various domains such as medical diagnosis and sensor analytics. Therefore, one might find it useful to be able to estimate the value of these functions in the presence of partially observed data. In this work, we present methodologies to estimate the Gaussian Kernel, the Euclidean Distance, the Epanechnikov kernel and arbitrary basis functions in the presence of possibly incomplete feature vectors.

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Section: Summary Of Publicationsmentioning

confidence: 99%

Machine Learning for Incomplete Data

Mesquita¹,

Gomes²

2018

Anais Do Concurso De Teses E Dissertações Da SBC (CTD-SBC)

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“…ELM is a powerful tool used in complex classification problems such as medical data classification [8], medical diagnostics [9], image quality assessment [10, 11], and many more. Few researchers have also applied ELM over SDP [12, 13] and cross‐project defect prediction [14].…”

Section: Introductionmentioning

confidence: 99%

Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

2020

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Predicting defects during software testing reduces an enormous amount of testing effort and help to deliver a high‐quality software system. Owing to the skewed distribution of public datasets, software defect prediction (SDP) suffers from the class imbalance problem, which leads to unsatisfactory results. Overfitting is also one of the biggest challenges for SDP. In this study, the authors performed an empirical study of these two problems and investigated their probable solution. They have conducted 4840 experiments over five different classifiers using eight NASA projects and 14 PROMISE repository datasets. They suggested and investigated the varying kernel function of an extreme learning machine (ELM) along with kernel principal component analysis (K‐PCA) and found better results compared with other classical SDP models. They used the synthetic minority oversampling technique as a sampling method to address class imbalance problems and k‐fold cross‐validation to avoid the overfitting problem. They found ELM‐based SDP has a high receiver operating characteristic curve over 11 out of 22 datasets. The proposed model has higher precision and F‐score values over ten and nine, respectively, compared with other state‐of‐the‐art models. The Mathews correlation coefficient (MCC) of 17 datasets of the proposed model surpasses other classical models' MCC.

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“…Encouraging results in prior research indicate that it is possible to predict which modules are likely to be locations of defect occurrence using Random Forest ( [10], [11]), Neural Network ( [12], [13]), Support Vector Machines ( [14], [15]), Logistic Regression ( [16]- [18]), and Naive Bayes ( [19]- [21]). In addition, there has been research on defect classification ( [22], [23]), justin-time defect prediction [24], and so on.…”

Section: Introductionmentioning

confidence: 99%

What Ruined Your Cake: Impacts of Code Modifications on Bug Distribution

Guo

Wong

2020

IEEE Access

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In the current decade, software systems have been more intensively employed in every aspect of our lives. However, it is disappointing that the quality of software is far from satisfactory. More importantly, the complexity and size of today's software systems are increasing dramatically, which means that the number of required modifications is also increasing exponentially. Therefore, it is necessary to understand how function-level modifications impact the distribution of software bugs. In addition, other factors such as a function's structural characteristics as well as attributes of functions themselves may also serve as informative indicators for software fault prediction. In this paper, we perform statistical methods and logistic regression to analyze the possible factors that are related to the distribution of software bugs. We demonstrate our study from the following five perspectives: 1) the distribution of bugs in time and space; 2) the distribution of function-level modifications in time and space; 3) the relationship between function-level modifications and functions' fault-proneness; 4) the relationship between functional attributes and functions' fault-proneness; and 5) the relationship between software structural characteristics and functions' fault-proneness. INDEX TERMS Bug, bug distribution, defected software systems, modifications, open source projects.

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Classification with reject option for software defect prediction

Cited by 35 publications

References 32 publications

Machine Learning for Incomplete Data

Machine Learning for Incomplete Data

Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

What Ruined Your Cake: Impacts of Code Modifications on Bug Distribution

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