An Experimental Assessment of Using Theoretical Defect Predictors to Guide Search-Based Software Testing

Perera, Anjana; Aleti, Aldeida; Turhan, Burak; Boehme, M.H.

doi:10.1109/tse.2022.3147008

Cited by 7 publications

(1 citation statement)

References 55 publications

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“…The manual detection and correction of defects incur significant labor and cost burdens. Therefore, software defect prediction has emerged as a promising approach to automatically predict defective modules with existing software code and historical data [2], [3], [4], aiding developers in cutting costs and enhancing development quality. Prior work indicates that software defect prediction has been a top three research priority in software engineering [5].…”

Section: Introductionmentioning

confidence: 99%

Software Defect Prediction via Generative Adversarial Networks and Pre-Trained Model

Song,

Gan,

Bao

2024

IJACSA

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Software defect prediction, which aims to predict defective modules during software development, has been implemented to assist developers in identifying defects and ensure software quality. Traditional defect prediction methods utilize manually designed features such as "Lines Of Code" that fail to capture the syntactic and semantic structures of code. Moreover, the high cost and difficulty of building the training set lead to insufficient data, which poses a significant challenge for training deep learning models, particularly for new projects. To overcome the practical challenge of data limitation and improve predictive capacity, this paper presents DP-GANPT, a novel defect prediction model that integrates generative adversarial networks and state-of-the-art code pre-trained models, employing a novel bi-modal code-prompt input representation. The proposed approach explores the use of code pre-trained model as autoencoders and employs generative adversarial networks algorithms and semi-supervised learning techniques for optimization. To facilitate effective training and evaluation, a new software defect prediction dataset is constructed based on the existing PROMISE dataset and its associated engineering files. Extensive experiments are performed on both within-project and cross-project defect prediction tasks to evaluate the effectiveness of DP-GANPT. The results reveal that DP-GANPT outperforms all the state-of-theart baselines, and achieves performance comparable to them with significantly less labeled data.

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Section: Introductionmentioning

confidence: 99%

Software Defect Prediction via Generative Adversarial Networks and Pre-Trained Model

Song,

Gan,

Bao

2024

IJACSA

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DPFuzz: A fuzz testing tool based on the guidance of defect prediction

Cui,

Jin,

Chen

et al. 2024

Science of Computer Programming

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Software defect prediction using learning to rank approach

Nassif,

Talib,

Azzeh

et al. 2023

Sci Rep

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Software defect prediction (SDP) plays a significant role in detecting the most likely defective software modules and optimizing the allocation of testing resources. In practice, though, project managers must not only identify defective modules, but also rank them in a specific order to optimize the resource allocation and minimize testing costs, especially for projects with limited budgets. This vital task can be accomplished using Learning to Rank (LTR) algorithm. This algorithm is a type of machine learning methodology that pursues two important tasks: prediction and learning. Although this algorithm is commonly used in information retrieval, it also presents high efficiency for other problems, like SDP. The LTR approach is mainly used in defect prediction to predict and rank the most likely buggy modules based on their bug count or bug density. This research paper conducts a comprehensive comparison study on the behavior of eight selected LTR models using two target variables: bug count and bug density. It also studies the effect of using imbalance learning and feature selection on the employed LTR models. The models are empirically evaluated using Fault Percentile Average. Our results show that using bug count as ranking criteria produces higher scores and more stable results across multiple experiment settings. Moreover, using imbalance learning has a positive impact for bug density, but on the other hand it leads to a negative impact for bug count. Lastly, using the feature selection does not show significant improvement for bug density, while there is no impact when bug count is used. Therefore, we conclude that using feature selection and imbalance learning with LTR does not come up with superior or significant results.

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An Experimental Assessment of Using Theoretical Defect Predictors to Guide Search-Based Software Testing

Cited by 7 publications

References 55 publications

Software Defect Prediction via Generative Adversarial Networks and Pre-Trained Model

Software Defect Prediction via Generative Adversarial Networks and Pre-Trained Model

DPFuzz: A fuzz testing tool based on the guidance of defect prediction

Software defect prediction using learning to rank approach

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