Joint feature representation learning and progressive distribution matching for cross-project defect prediction

Zou, Quanyi; Lu, Lu; Yang, Zhengming; Gu, Xiaowei; Qiu, Shaojian

doi:10.1016/j.infsof.2021.106588

Cited by 24 publications

(37 citation statements)

References 47 publications

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“…Related WorkCompared with the relatively mature open-sourced code diffing technology[24][25][26][27][28][29][30], binary diffing analysis method faces code optimization problems such as function inlining, redundancy elimination, instruction reordering and conversion in the compilation process. These problems make binary diffing more difficult than open-sourced files.…”

mentioning

confidence: 99%

A Heuristic Local-sensitive Program-Wide Diffing Method for IoT Binary Files

Shen

et al. 2021

Arab J Sci Eng

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Code reuse brings vulnerabilities in third-party library to many Internet of Things (IoT) devices, opening them to attacks such as distributed denial of service. Program-wide binary diffing technology can help detect these vulnerabilities in IoT devices whose source codes are not public. Considering the architectures of IoT devices may vary, we propose a data-aware program-wide diffing method across architectures and optimization levels. We rely on the defined anchor functions and call relationship to expand the comparison scope within the target file, reducing the impact of different architectures on the diffing result. To make the diffing result more accurate, we extract the semantic features that can represent the code by data flow dependence analysis. Earth mover distance is used to calculate the similarity of functions in two files based on semantic features. We implemented a proof-of-concept DAPDiff and compared it with baseline BinDiff, TurboDiff and Asm2vec. Experiments showed the availability and effectiveness of our method across optimization levels and architectures. DAPDiff outperformed BinDiff in recall and precision by 41.4% and 9.2% on average when making diffing between standard third-party library and the real-world firmware files. This proves that DAPDiff can be applicable for the vulnerability detection in IoT devices.

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mentioning

confidence: 99%

A Heuristic Local-sensitive Program-Wide Diffing Method for IoT Binary Files

Shen

et al. 2021

Arab J Sci Eng

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“…Gong et al [47] utilized the thought of stratification embedded in the nearest neighbor to produce evolving training datasets with balanced data. Zou et al [48] proposed a method named Joint Feature representation with double marginalized denoising auto-encoders to learn the global and local features, and they introduced local data gravitation between source and target domains to determine instance weight in the learning process (Zou et al [49]). Jin et al [50] used two support vector machines to implement domain adaptation to match data distribution.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Improving Homogeneous Cross-Project Defect Prediction by Jensen-Shannon Divergence and Relative Density

Ren

Peng

Zheng

et al. 2022

Scientific Programming

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Homogeneous cross-project defect prediction (HCPDP) aims to apply a binary classification model built on source projects to a target project with the same metrics. However, there is still room for improvement in the performance of the existing HCPDP models. This study has proposed a novel approach, including one-to-one and many-to-one predictions. First, we apply the Jensen-Shannon divergence to select the most similar source project automatically. Second, relative density estimation is introduced to choose the suitable instance of the selected source project. Third, one-to-one and many-to-one prediction models are trained by the selected instances. Finally, two benchmark datasets are used to evaluate the proposed approach. Compared to the state-of-the-art methods, the experimental results demonstrated that the proposed approach could improve the prediction performance in the F1-score, AUC, and G-mean metrics and exhibit strong adaptability to the traditional classifiers.

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“…Their method discarded so much useful information and performed worse than the compared methods in defect prediction. Zou et al 24 combined joint feature representation with double marginalized denoising autoencoders (DMDA_JFR) for CPDP. Besides, they used class label information of source data to obtain local transferable features.…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of these methods for CPDP relies heavily on the matching results between source and target projects. Besides, few methods leveraged class label information of source data except Bellwether 23 and DMDA_JFR 24 . The class labels of training data might help reduce the ambiguous predictions near the decision boundary 24 .…”

Section: Introductionmentioning

confidence: 99%

WIFLF: An approach independent of the target project for cross‐project defect prediction

Cui

Liu

Wang

2022

J Software Evolu Process

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Cross-project defect prediction (CPDP) is used to build defect prediction models when data from the target project are not enough. There has been several approaches to improve the performance of CPDP, such as feature transformation and instance selection methods. However, existing techniques are strongly dependent on the target data to reduce the distribution discrepancy between source and target projects. That is, the performance of these methods is determined by the effectiveness of feature transformation or the similarity between two projects. Additionally, when there is a large amount of source data that needs to be matched with target data, it will take much time and reduce the efficiency of model construction.Therefore, it is vital to explore a target project-agnostic approach to build CPDP models. This paper presents a Weighted Isolation Forest with class Label information Filter (WIFLF) to relieve the issues above. Four groups of datasets from AEEEM, Relink and PROMISE Data Repository are used to conduct CPDP models. Besides, WIFLF is compared with 12 approaches. The experimental results indicate that WIFLF significantly outperforms all the baselines. Specifically, WIFLF with random forest significantly improves the performance over the baselines on average by at least 14.64% and 4.90% with respect to Skewed F-Measure and G-Measure, respectively.

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Joint feature representation learning and progressive distribution matching for cross-project defect prediction

Cited by 24 publications

References 47 publications

A Heuristic Local-sensitive Program-Wide Diffing Method for IoT Binary Files

A Heuristic Local-sensitive Program-Wide Diffing Method for IoT Binary Files

An Approach to Improving Homogeneous Cross-Project Defect Prediction by Jensen-Shannon Divergence and Relative Density

WIFLF: An approach independent of the target project for cross‐project defect prediction

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