Software Defect Prediction Model Based on Improved Deep Forest and AutoEncoder by Forest

Zheng, Wenbo; Mo, Shaocong; Jin, Xin; Qu, Yili; Xie, Zefeng; Su, Jia

doi:10.18293/seke2019-008

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Deep forests consist of layer-by-layer structures known as cascade forests. The structure of each layer in the cascade forest resembles the backpropagation of DNNs, with the distinction that it contains multiple Random Forests instead of neurons [3]. Cascade forest refers to a class distribution each tree generates for every instance.…”

Section: Learning and Hyperparameter Tuningmentioning

confidence: 99%

“…Early identification of software defects can curtail development expenses and rework efforts and yield more dependable software [2]. Predicting software defects is of utmost significance to address software issues while enhancing software quality [3]. The prediction of software defects involves scrutinizing software metrics and subsequently constructing models for defect prognostication.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction

Rizky,

Faisal,

Budiman

et al. 2024

Indonesian J. Comput. Cybern. Syst.

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The field of information technology requires software, which has significant issues. Quality and reliability improvement needs damage prediction. Tree-based algorithms like Random Forest, Deep Forest, and Decision Tree offer potential in this domain. However, proper hyperparameter configuration is crucial for optimal outcomes. This study demonstrates the use of Random Search Hyperparameter Setting Technique to predict software defects, improving damage estimation accuracy. Using ReLink datasets, we found effective algorithm parameters for predicting software damage. Decision Tree, Random Forest, and Deep Forest achieved an average AUC of 0.73 with Random Search. Random Search outperformed other tree-based algorithms. The main contribution is the innovative Random Search hyperparameter tuning, particularly for Random Forest. Random Search has distinct advantages over other tree-based algorithms

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Section: Learning and Hyperparameter Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction

Rizky,

Faisal,

Budiman

et al. 2024

Indonesian J. Comput. Cybern. Syst.

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show abstract

“…Turunnya kualitas software menjadi suatu kerugian tersendiri. Sangat penting untuk melakukan prediksi cacat software sebagai upaya menangani masalah software sekaligus dapat meningkatkan kualitas software [3]. Prediksi cacat software dilakukan dengan cara menganalisis metrik software kemudian dilanjutkan dengan membangun model untuk memprediksi cacat.…”

Section: Pendahuluanunclassified

Peningkatan Kinerja Prediksi Cacat Software Dengan Hyperparameter Tuning Pada Algoritma Klasifikasi Deep Forest

et al. 2022

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Prediksi cacat software adalah salah satu studi pada bidang Rekayasa Perangkat Lunak yang telah diteliti oleh banyak peneliti. Tujuan dari studi ini adalah untuk mencari tahu algoritma yang dapat memberikan kinerja prediksi cacat software yang lebih baik. Salah satu penelitian yang telah dilakukan adalah melakukan prediksi cacat software dengan menggunakan algoritma berbasis pohon seperti Decision Tree, Random Forest dan Deep Forest. Deep Forest adalah algoritma klasifikasi berbasis pohon yang baru yang merupakan perbaikan dari algoritma Random Forest. Namun implementasi Deep Forest dalam penelitian terdahulu masih belum memberikan kinerja yang maksimal. Hasil pada penelitian terdahulu menunjukan bahwa kinerja algoritma Deep Forest masih ada yang lebih rendah dibandingkan algoritma berbasis pohon yang lain. Pada penelitian ini berfokus pada peningkatan kinerja algoritma berbasis pohon dengan melakukan normalisasi pada dataset dan hyperparameter tuning pada algoritma klasifikasi dengan menggunakan pencarian grid. Dataset yang digunakan adalah 3 dataset dari ReLink yaitu Apache, Safe, dan Zxing. Setiap model prediksi divalidasi dengan Stratified 10-Fold Cross Validation dan kinerja dievaluasi menggunakan AUC. Dari hasil eksperimen yang didapatkan,hasil prediksi dari pendekatan yang diusulkan lebih baik daripada metode sebelumnya.

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“…The results on 25 projects showed that their method was more effective to identify defective software modules in terms of area under the receiver operating characteristic curve indicator. Zheng et al [32] employed an improved deep forest method based on data augmentation and autoencoder techniques for predicting software defects. They conducted experiments on Eclipse project and the results showed that their approach achieved better performance than original deep forest method.…”

Section: Deep Forest In Software Engineeringmentioning

confidence: 99%

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

Zhao

Liu

Zhou

et al. 2021

Preprint

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The software inevitably encounters the crash, which will take developers a large amount of effort to find the fault causing the crash (short for crashing fault). Developing automatic methods to identify the residence of the crashing fault is a crucial activity for software quality assurance. Researchers have proposed methods to predict whether the crashing fault resides in the stack trace based on the features collected from the stack trace and faulty code, aiming at saving the debugging effort for developers. However, previous work usually neglected the feature preprocessing operation towards the crash data and only used traditional classification models. In this paper, we propose a novel crashing fault residence prediction framework, called ConDF, which consists of a consistency based feature subset selection method and a state-of-the-art deep forest model. More specifically, first, the feature selection method is used to obtain an optimal feature subset and reduce the feature dimension by reserving the representative features. Then, a simplified deep forest model is employed to build the classification model on the reduced feature set. The experiments on seven open source software projects show that our ConDF method performs significantly better than 17 baseline methods on three performance indicators.

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Software Defect Prediction Model Based on Improved Deep Forest and AutoEncoder by Forest

Cited by 8 publications

References 19 publications

Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction

Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction

Peningkatan Kinerja Prediksi Cacat Software Dengan Hyperparameter Tuning Pada Algoritma Klasifikasi Deep Forest

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

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