A Public Unified Bug Dataset for Java

Ferenć, Rudolf; Tóth, Zoltán; Ladányi, Gergely; Siket, István; Gyimóthy, Tibor

doi:10.1145/3273934.3273936

Cited by 41 publications

(26 citation statements)

References 35 publications

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“…For experimentation, we considered forty open source datasets relating to the defect prediction from tera-PROMISE repository [39]. The list and the class imbalance percentages of datasets are shown in Table 1.…”

Section: Experimentation and Resultsmentioning

confidence: 99%

Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

2020

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Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in the literature suffer from the class imbalance problem. In this paper, a novel class imbalance reduction (CIR) algorithm is proposed to create a symmetry between the defect and non-defect records in the imbalance datasets by considering distribution properties of the datasets and is compared with SMOTE (synthetic minority oversampling technique), a built-in package of many machine learning tools that is considered a benchmark in handling class imbalance problems, and with K-Means SMOTE. We conducted the experiment on forty open source software defect datasets from PRedict or Models in Software Engineering (PROMISE) repository using eight different classifiers and evaluated with six performance measures. The results show that the proposed CIR method shows improved performance over SMOTE and K-Means SMOTE.

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Section: Experimentation and Resultsmentioning

confidence: 99%

Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

2020

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“…The PROMISE dataset has been validated and used several times in different research papers, e.g., concerning bug prediction [15,14].…”

Section: G Defectsmentioning

confidence: 99%

“…The process of detecting and removing soft-ware defects is an important step in guaranteeing the fulfilment of end user satisfaction [20] and reducing the economic liability associated with releasing flawed software products [26]. Furthermore, building an efficient defect prediction model has raised increased interest from researchers in their quest to learn from the pre-70 vious defects and to use this knowledge to predict future ones [15]. Defects were also heavily investigated in the literature, with several studies considering defects as a dependent variable affected by various independent variables, such 75 as patterns [52,22,6], bad design [57] and the presence of code smells [30].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Code Smells on the Relationship Between Design Patterns and Defects

Alkhaeir¹,

Walter²

2021

IEEE Access

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“…However, public datasets may have some issues such as lack of data quality. For example, there are two versions of PROMISE (Ferenc et al, 2018) dataset. Simplified PROMISE Source Code (SPSC) and PROMISE Source Code (PSC).…”

Section: B Datasetsmentioning

confidence: 99%

A Systematic Literature Review of Software Defect Prediction Using Deep Learning

Fathy¹,

Abd-Elmegid²,

Bahaa³

et al. 2021

Journal of Computer Science

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The approaches associated with software defect prediction are used to reduce the time and cost of discovering software defects in source code and to improve the software quality in the organizations. There are two approaches to reveal the software defects in the source code. The first approach is concentrated on the traditional features such as lines of code, code complexity, etc. However, these features fail to extract the semantics of the source code. The second one is concentrated on revealing these semantics. This paper presents a Systematic Literature Review (SLR) of software defect prediction using deep learning models. This SLR is focused on identifying the studies that use the semantics of the source code for improving defect prediction. This SLR aims to analyze the used datasets, models and frameworks. Also, identifying the evaluation metrics to ensure their applicability in software defect prediction. IEEE Xplore, Scopus and Web of Science digital libraries were used to select the suitable primary studies. Forty (40) primary studies were selected that published by 15 December 2020 for analysis based on the quality criteria. The project levels that applied in the studies were: Within-project 52.5%, cross-project 17.5% and both within-project and cross-project 30%. The datasets used were: Promise dataset 68.18% and other datasets 31.82%. The most used deep learning model in the primary studies was: Convolutional Neural Network (CNN) by 35%. The most used evaluation metrics were: F-measure and Area Under the Curve (AUC). Software defect prediction using deep learning models is still a valuable topic and requires much research studies to enhance the performance of the defect prediction.

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A Public Unified Bug Dataset for Java

Cited by 41 publications

References 35 publications

Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

The Effect of Code Smells on the Relationship Between Design Patterns and Defects

A Systematic Literature Review of Software Defect Prediction Using Deep Learning

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