Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 250200052-11.doi: 10.11896/jsjkx.250200052

• Computer Software & Architecture • Previous Articles     Next Articles

Review of Development and Application of Software Defect Prediction Techniques in IndustrialInternet of Things Environment

DENG Tao1, DENG Ye2   

  1. 1 School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China
    2 School of Electronics and Information Engineering,Wuzhou University,Wuzhou,Guangxi 543003,China
  • Online:2025-11-15 Published:2025-11-10

PDF (PC)

13

Abstract: In the context of IIoT,the generation of vast amounts of software code data necessitates effective analysis through advanced SDP techniques.These techniques not only enable the rapid identification of anomalies but also facilitate comprehensive investigations into potential issues,as even minor deviations can lead to significant code failures.This paper systematically reviews over 61 relevant articles published between 2018 and 2025,highlighting the primary challenges and recent advancements in SDP within IIoT.Various perspectives on SDP technologies are explored,including statistical methods,machine learning approaches,and model-oriented techniques.Future research should prioritize the dynamics of defect patterns in complex heterogeneous environments,address the challenges of data scarcity and high labeling costs,and balance the trade-off between real-time processing and resource constraints.Additionally,the interpretability of models and user cognitive understanding must be enhanced to improve system comprehensibility and operational robustness.A comprehensive analysis of existing datasets related to IIoT is also presented,laying a solid foundation for further research in this critical area.

Key words: Industrial Internet of Things, Software defect prediction, Model-oriented

CLC Number: 

  • TP311
[1]STRANDBERG P E,AFZAL W,SUNDMARK D.Software test results exploration and visualization with continuous integration and nightly testing[J].International Journal on Software Tools for Technology Transfer,2022,24(2):261-285.
[2]AL QASEM O,AKOUR M,ALENEZIM.The influence of deep learning algorithms factors in software fault prediction[J].IEEE Access,2020,8:63945-63960.
[3]BHANDARI K,KUMAR K,SANGALA L.Data quality issues in software fault prediction:a systematic literature review[J].Artificial Intelligence Review,2023,56(8):7839-7908.
[4]PANDEY S,KUMAR K.Software Fault Prediction for Imbalanced Data:A Survey on Recent Developments[J].Procedia Computer Science,2023,218:1815-1824.
[5]SRIVASTAVA V,CHAUHAN R K,LOHIA P.Highly effi-cient cesium-based halide perovskite solar cell using SCAPS-1D software:theoretical study[J].Journal of Optics,2023,52(3):1218-1225.
[6]DEVALE P,JADHAV R B,BIDWE R V,et al.Machine Learning and Just-in-Time Strategies for Effective Bug Tracking in Software Development[J].International Journal of Intelligent Systems and Applications in Engineering,2024,12(6s):749-758.
[7]ZHENG W,SHEN T,CHEN X,et al.Interpretability application of the Just-in-Time software defect prediction model[J].Journal of Systems and Software,2022,188:111245.
[8]GOYAL S.Static code metrics-based deep learning architecture for software fault prediction[J].Soft Computing,2022,26(24):13765-13797.
[9]SATAPATHY S C,JENA A K,SINGH J,et al.A novel approach of software fault prediction using deep learning technique[J].Automated Software Engineering:A Deep Learning-Based Approach,2020:73-91.
[10]KHAN B,IQBAL D,BADSHAH S.Cross-Project SoftwareFault Prediction Using Data Leveraging Technique to Improve Software Quality[C]//Proceedings of the 24th International Conference on Evaluation and Assessment in Software Engineering.2020:434-438.
[11]WU F J.Research progress on static software defect prediction[J].Journal of Computer Science and Exploration,2019,13(10):1621-1637.
[12]LI Y,LIU Z D,ZHANG H J.A survey on cross-project software defect prediction methods[J].Computer Technology and Development,2020,30(3):98-103,121.
[13]LIU X T,GUO Z Q,LIU S R,et al.Comparative experiments among software defect prediction models:Issues,progress and challenges[J].Journal of Software,2023,34(2):582-624.
[14]CAI L,FAN Y R,YAN M,et al Research progress on just-in-time software defect prediction[J].Journal of Software,2019,30(5):1288-1307.
[15]BATOOL I,KHAN T A.Software fault prediction using deep learning techniques[J].Software Quality Journal,2023,31:1-40.
[16]ZAIN Z M,SAKRI S,ISMAILN H A.Application of DeepLearning in Software Defect Prediction:Systematic Literature Review and Meta-analysis[J].Information and Software Technology,2023,158:107175.
[17]PANDEY S K,MISHRA R B,TRIPATHI A K.Machine learning based methods for software fault prediction:A survey[J].Expert Systems with Applications,2021,172:114595.
[18]RATHORE S S,KUMAR S.A study on software fault prediction techniques[J].Artificial Intelligence Review,2019,51:255-327.
[19]LIU Y,LIU B.A modified uncertain maximum likelihood estimation with applications in uncertain statistics[J].Communications in Statistics-Theory and Methods,2024,53(18):6649-6670.
[20]SAINSBURY-DALE M,ZAMMIT-MANGION A,RICHARDS J,et al.Neural Bayes estimators for irregular spatial data using graph neural networks[J].Journal of Computational and Graphical Statistics,2025,34:1-16.
[21]AL-OMARI S,ELSHEIKH Y,AZZEH M.A New Learning to Rank Approach for Software Defect Prediction[J].International Journal of Advanced Computer Science and Applications,2022,13(8).
[22]SIVAVELU S,PALANISAMY V.Piecewise Congruence Re-gressed Indexive Extreme Learning Classifier for Software Fault Prediction[J].IEEE Access,12.
[23]KHAN B,IQBAL D,BADSHAH S.Cross-Project SoftwareFault Prediction Using Data Leveraging Technique to Improve Software Quality[C]//Proceedings of the 24th International Conference on Evaluation and Assessment in Software Engineering.2020:434-438.
[24]WANG H,KHOSHGOFTAAR T M.A study on software metric selection for software fault prediction[C]//18th IEEE International Conference On Machine Learning And Applications(ICMLA 2019).IEEE,2019:1045-1050.
[25]LI L,NAHAYO L,HABIYAREMYE G,et al.Applicability and performance of statistical index,certain factor and frequency ratio models in mapping landslides susceptibility in Rwanda[J].Geocarto International,2022,37(2):638-656.
[26]RATHORE S S.An exploratory analysis of regression methods for predicting faults in software systems[J].Soft Computing,2021,25:14841-14872.
[27]SINGH R,RATHORE S S.Linear and non-linear bayesian regression methods for software fault prediction[J].International Journal of System Assurance Engineering and Management,2022,13(4):1864-1884.
[28]HUANG X W,FAN G S,YU H Q,et al.Software defect prediction based on heavy sub-node abstract syntax trees[J].Journal of Computer Engineering,2025,42(1):25-2,48.
[29]SURYAWANSHI R,KADAM A.Software Defect Prediction byLogistic Regression with Gradient Descent Cost Computation[C]//International Conference on Emerging Smart Computing and Informatics(ESCI )2024.IEEE,2024:1-5.
[30]PRITCHARD S,MITRA B,NAGARAJU V.Three-Stage Ad-justed Regression Forecasting for Software Defect Prediction[C]//Annual Reliability and Maintainability Symposium(RAMS 2024).IEEE,2024:1-6.
[31]YU X,KEUNG J,XIAO Y,et al.Predicting the precise number of software defects:Are we there yet?[J].Information and Software Technology,2022,146:106847.
[32]HABTEMARIAM G M,MOHAPATRA S K.A genetic algo-rithm-based approach for test case prioritization[C]//Information and Communication Technology for Development for Africa:Second International Conference(ICT4DA 2019).Bahir Dar,Ethiopia,Revised Selected Papers 2.Springer International Publishing,2019:24-37.
[33]SREEDEVI E,PREMALATHA V,SIVAKUMAR S,et al.Acomparative study on new classification algorithm using NASA MDP datasets for software defect detection[C]//International Conference on Intelligent Sustainable Systems(ICISS 2019).IEEE,2019:312-317.
[34]XU Z,LIU J,LUO X,et al.Cross-version defect prediction via hybrid active learning with kernel principal component analysis[C]//IEEE 25th international conference on software analysis,evolution and reengineering(SANER 2018).IEEE,2018:209-220.
[35]SHEN Y,HU S,CAI S,et al.Software Defect Prediction based on Bayesian Optimization Random Forest[C]//9th International Conference on Dependable Systems and Their Applications(DSA 2022).IEEE,2022:1012-1013.
[36]WEI H,HU C,CHEN S,et al.Establishing a software defectprediction model via effective dimension reduction[J].Information Sciences,2019,477:399-409.
[37]BALARAM A,VASUNDRA S.Prediction of software fault-prone classes using ensemble random forest with adaptive synthetic sampling algorithm[J].Automated Software Engineering,2022,29(1):6.
[38]GOYAL S.Effective software defect prediction using supportvector machines(SVMs)[J].International Journal of System Assurance Engineering and Management,2022,13(2):681-696.
[39]ZHANG J,LI D,WONG W E,et al.A Hybrid Sampling andMulti-Objective Optimization Approach for Enhanced Software Defect Prediction[J].arXiv:2410.10046,2024.
[40]ZHANG Y.Software defect prediction model based on deeplearning[C]//International Conference on Power,Electrical Engineering,Electronics and Control(PEEEC 2024).IEEE,2024:954-959.
[41]ZHANG S,JIANG S,YAN Y.A Hierarchical Feature Ensemble Deep Learning Approach for Software Defect Prediction[J].International Journal of Software Engineering and Knowledge Engineering,2023,33(4):543-573.
[42]CUI M T,WU K Q,MARIANI M S. Software defect prediction based on feature extraction and Stacking ensemble learning[J].Computer Applications and Software,2021,47(12):230-235,248.
[43]LIU C,SANOBER S,ZAMANI A S,et al.Defect PredictionTechnology in Software Engineering Based on Convolutional Neural Network[J].Security and Communication Networks,2022,2022(1):5058461.
[44]LIN H,LI B,WANG X,et al.Automated defect inspection of LED chip using deep convolutional neural network[J].Journal of Intelligent Manufacturing,2019,30:2525-2534.
[45]QIU X,FAN P,REN J.Convolutional Neural Network-BasedResearch on Software Engineering Defect Prediction[C]//Proceedings of the 6th International Conference on Information Technologies and Electrical Engineering.2023:305-308.
[46]ZENG C,ZHOU C Y,LV S K,et al.Gcn2defect:Graph convolutional networks for smotetomek-based software defect prediction[C]//IEEE 32nd International Symposium on Software Reliability Engineering(ISSRE 2021).IEEE,2021:69-79.
[47]KHLEEL N A A,NEHÉZ K.A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method[J].Journal of Intelligent Information Systems,2023,60(3):673-707.
[48]QIU S,LU L,CAI Z,et al.Cross-Project Defect Prediction viaTransferable Deep Learning-Generated and Handcrafted Features[C]//SEKE.2019:431-552.
[49]SEKARAN K,ANNABEL L S P.A Deep Learning Based Model for Defect Prediction in Intra-Project Software[C]//7th International Conference on Trends in Electronics and Informatics(ICOEI 2023).IEEE,2023:1148-1155.
[50]NEVENDRA M,SINGH P.Defect count prediction via metric-based convolutional neural network[J].Neural Computing and Applications,2021,33(22):15319-15344.
[51]BALASUBRAMANIAM S,GOLLAGI S G.Software defectprediction via optimal trained convolutional neural network[J].Advances in Engineering Software,2022,169:103138.
[52]LI X,ZHU Z.Software Defect Detection Based on Feature Fusion and Alias Analysis[C]//IEEE International Test Conference in Asia(ITC-Asia 2023).IEEE,2023:1-6.
[53]KHLEEL N A A,NEHÉZ K.Software defect prediction using a bidirectional LSTM network combined with oversampling techniques[J].Cluster Computing,2024,27(3):3615-3638.
[54]PANDEY S K,TRIPATHIA K.Cross-Project setting usingDeep learning Architectures in Just-In-Time Software Fault Prediction:An Investigation[C]//IEEE/ACM International Conference on Automation of Software Test(AST 2023).IEEE,2023:24-34.
[55]FAN G,DIAO X,YU H,et al.Software defect prediction via attention-based recurrent neural network[J].Scientific Programming,2019,2019(1):6230953.
[56]DAM H K,PHAM T,NG S W,et al.A deep tree-based model for software defect prediction[J].arXiv:1802.00921,2018.
[57]BORANDAG E.Software fault prediction using an RNN-based deep learning approach and ensemble machine learning techniques[J].Applied Sciences,2023,13(3):1639.
[58]WANG H,ZHUANG W,ZHANG X.Software defect prediction based on gated hierarchical LSTMs[J].IEEE Transactions on Reliability,2021,70(2):711-727.
[59]MUNIR H S,REN S,MUSTAFA M,et al.Attention basedGRU-LSTM for software defect prediction[J].Plos One,2021,16(3):e0247444.
[60]FARID A B,FATHY E M,ELDINA S,et al.Software defect prediction using hybrid model(CBIL) of convolutional neural network(CNN) and bidirectional long short-term memory(Bi-LSTM)[J].PeerJ Computer Science,2021,7:e739.
[61]LUO N,MA Y,LI J,et al.A Just-in-time Software Defect Detection Method Using Generative Adversarial Networks[C]//4th International Conference on Electronic Communication and Artificial Intelligence(ICECAI 2023).IEEE,2023:37-45.
[62]CHOUHAN S S,RATHORE S S.Generative adversarial net-works-based imbalance learning in software aging-related bug prediction[J].IEEE Transactions on Reliability,2021,70(2):626-642.
[63]KUMAR P S,VENKATESAN R.IMproving software defectprediction using generative adversarial networks[J].Int.J.Sci.Eng.Appl,2020,9:117-120.
[64]PAL S.Generative adversarial network-based cross-project fault prediction[J].arXiv:2105.07207,2021.
[65]XU J P,GUO X F,WANG R B,et al.Software defect prediction aggregation model based on GAN data augmentation[J].Computer Science,2023,50(12):24-31.
[66]ZHANG H W,JIA X Y.Oversampling method for class-imba-lanced software defect prediction based on generative adversarial networks[J].Journal of Nanjing University of Science and Technology,,2023,47(2):174-182.
[67]SONG W,GAN L,BAO T.Software Defect Prediction via Ge-nerative Adversarial Networks and Pre-Trained Model[J].International Journal of Advanced Computer Science & Applications,2024,15(3).
[68]MAO J E,ZHOU S J,ZHANG S Q,et al.Software defect prediction method based on attention and cost sensitivity[J].Computer Measurement & Control,2024,32(9):94-100.
[69]QU T,LIU W,ZHENG W,et al.Software Defect DetectionMethod Based on Graph Structure and Deep Neural Network[C]//International Conference on Asian Language Processing(IALP 2022).IEEE,2022:395-400.
[70]MASHHADI E,AHMADVAND H,HEMMATI H.Method-level bug severity prediction using source code metrics and LLMs[C]//IEEE 34th International Symposium on Software Reliability Engineering(ISSRE 2023).IEEE,2023:635-646.
[71]SULTAN M F,KARIM T,SHAON M S H,et al.EnhancedLLM-Based Framework for Predicting Null Pointer Dereference in Source Code[J].arXiv:2412.00216,2024.
[72]NAKHLA RAFI M,KIM D J,CHENT H,et al.EnhancingFault Localization Through Ordered Code Analysis with LLM Agents and Self-Reflection[J].arXiv:2409.13642,2024.
[73]HOSSAIN S B,JIANG N,ZHOU Q,et al.A deep dive into large language models for automated bug localization and repair[J].Proceedings of the ACM on Software Engineering,2024,1(FSE):1471-1493.
[74]HE Z,PETERS F,MENZIES T,et al.Learning from open-source projects:An empirical study on defect prediction[C]//2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement.IEEE,2013:45-54.
[75]COTRONEO D,NATELLA R,PIETRANTUONO R.Predicting aging-related bugs using software complexity metrics[J].Performance Evaluation,2013,70(3):163-178.
[76]MALHOTRA R,SHUKLA S,SAWHNEY G.Assessment of defect prediction models using machine learning techniques for object-oriented systems[C]//5th International Conference on Reliability,Infocom Technologies and Optimization(Trends and Future Directions)(ICRITO 2016).IEEE,2016:577-583.
[77]NASA Defect Dataset[OL].https://github.com/klainfo/NASADefectDataset.
[78]LECUN Y.The MNIST database of handwritten digits[J／OL].http://yann.lecun.com/exdb/mnist/,1998.
[79]NAIRKrizhevsky,HINTON Vinod,CHRISTOPHER Geoffrey,Mike Papadakis,and Anthony Ventresque.The cifar-10 dataset[EB／OL].http://www.cs.toronto.edu/kriz/ cifar.html.
[80]RUSSAKOVSKY O,DENG J,SU H,et al.Imagenet large scale visual recognition challenge[J].International Journal of Computer Vision,2015,115:211-252.
[81]OKUN V,DELAITRE A,BLACK P E.Report on the staticanalysis tool exposition(sate) iv[J].NIST Special Publication,2013,500:297.
[82]ZHANG C,XIANG J,HAO R,et al.SGT:Aging-related bugprediction via semantic feature learning based on graph-transformer[J].Journal of Systems and Software,2024,217:112156.
[83]LU G,LING F,LI J,et al.Graph Attention-Based Dual Enhancement for Multiview Clustering[J].IEEE Transactions on Computational Social Systems,2025:1-10.
[84]HE T,YANG M,HU W,et al.Analysis of the Effectiveness of Large Language Model Feature in Source Code Defect Detection[C]//3rd International Conference on Artificial Intelligence and Computer Information Technology(AICIT 2024).IEEE,2024:1-4.
[85]WEN X C,WANG X,CHEN Y,et al.Vuleval:Towards repository-level evaluation of software vulnerability detection[J].ar-Xiv:2404.15596,2024.
[86]SHANKAR MISHRA A,SINGH RATHORE S.Implicit andexplicit mixture of experts models for software defect prediction[J].Software Quality Journal,2023,31(4):1331-1368.
[87]JU E,LEE J,RYU D.DefectGRANDE:Hybrid Approach forClass Imbalance in Software Defect Prediction[C]//IEEE International Conference on Big Data and Smart Computing(BigComp 2025).IEEE,2025:90-91.
[88]LIU W,YUE Y,CHEN X,et al.SeDPGK:Semi-supervised software defect prediction with graph representation learning and knowledge distillation[J].Information and Software Technology,2024,174:107510.
[89]BHUSHAN M,DUARTE J Á G,NEGI A,et al.An ontological knowledge-based method for handling feature model defects due to dead feature[J].Engineering Applications of Artificial Intelligence,2024,136:109000.
[90]ASAL B,DEMIR M Ö.Enhancing Software Defect Predictionthrough Explainable AI:Integrating SHAP and LIME in a Vo-ting Classifier Framework[C]//8th International Artificial Intelligence and Data Processing Symposium(IDAP 2024).IEEE,2024:1-7.
[91]YAN Z,ZHANG L.Interpretable Wind Power Prediction:AMachine Learning Perspective Using Lightgbm and SHAP[C]//2nd International Conference on Artificial Intelligence and Automation Control(AIAC 2024).IEEE,2024:225-229.
[1] QIAO Yu, XU Tao, ZHANG Ya, WEN Fengpeng, LI Qiangwei. Graph Neural Network Defect Prediction Method Combined with Developer Dependencies [J]. Computer Science, 2025, 52(6): 52-57.
[2] ZHU Xiaoyan, WANG Wenge, WANG Jiayin, ZHANG Xuanping. Just-In-Time Software Defect Prediction Approach Based on Fine-grained Code Representationand Feature Fusion [J]. Computer Science, 2025, 52(1): 242-249.
[3] LI Huilai, YANG Bin, YU Xiuli, TANG Xiaomei. Explainable Comparison of Software Defect Prediction Models [J]. Computer Science, 2023, 50(5): 21-30.
[4] LI Xiaohuan, CHEN Bitao, KANG Jiawen, YE Jin. Coalition Game-assisted Joint Resource Optimization for Digital Twin-assisted Edge Intelligence [J]. Computer Science, 2023, 50(2): 42-49.
[5] XU Jinpeng, GUO Xinfeng, WANG Ruibo, LI Jihong. Aggregation Model for Software Defect Prediction Based on Data Enhancement by GAN [J]. Computer Science, 2023, 50(12): 24-31.
[6] WANG Zichen, YUAN Chengsheng, WANG Yili, GUO Ping, FU Zhangjie. Lightweight Group Key Agreement for Industrial Internet of Things [J]. Computer Science, 2023, 50(11A): 230700075-10.
[7] Ran WANG, Jiang-tian NIE, Yang ZHANG, Kun ZHU. Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids [J]. Computer Science, 2022, 49(6): 44-54.
[8] ZHANG Xiao-mei, CAO Ying, LOU Ping, JIANG Xue-mei, YAN Jun-wei, LI Da. Lossless Data Compression Method Based on Edge Computing [J]. Computer Science, 2022, 49(11A): 210500195-6.
[9] LI Bei-bei, SONG Jia-rui, DU Qing-yun, HE Jun-jiang. DRL-IDS:Deep Reinforcement Learning Based Intrusion Detection System for Industrial Internet of Things [J]. Computer Science, 2021, 48(7): 47-54.
[10] ZHENG Xiao-meng, GAO Meng, TENG Jun-yuan. Research on Construction Method of Defect Prediction Dataset for Spacecraft Software [J]. Computer Science, 2021, 48(6A): 575-580.
[11] WANG Wei-hong, CHEN Zhen-yu. Intelligent Manufacturing Security Model Based on Improved Blockchain [J]. Computer Science, 2021, 48(2): 295-302.
[12] TENG Jun-yuan, GAO Meng, ZHENG Xiao-meng, JIANG Yun-song. Noise Tolerable Feature Selection Method for Software Defect Prediction [J]. Computer Science, 2021, 48(12): 131-139.
[13] QIU Shao-jian, CAIZi-yi, LU Lu. Cost-sensitive Convolutional Neural Network Model for Software Defect Prediction [J]. Computer Science, 2019, 46(11): 156-160.
[14] HU Meng-yuan, HUANG Hong-yun, DING Zuo-hua. Ensemble Model for Software Defect Prediction [J]. Computer Science, 2019, 46(11): 176-180.
[15] XUE Can-guan, YAN Xue-feng. Software Defect Prediction Based on Improved Deep Forest Algorithm [J]. Computer Science, 2018, 45(8): 160-165.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.