Yang Zhang's homepage

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ABOUT

PERSONAL DETAILS

BIO

ABOUT ME

I am a professor and the Muxing scholar at School of Information Science and Engineering, Hebei University of Science and Technology. Currently, I am leading intelligent software Lab. I received my doctoral degree from Beijing Institute of Technology under the supervision of Prof. Feng Shi and Prof. Weixing Ji. I was a visiting scholar at Purdue University under the supervision of Prof. Xiangyu Zhang, and then at Federation University Australia (short-term) working with Prof. Guojun Lu and Dr. Grant Meredith. My research spans intelligent software engineering, software evolution, parallel programming, and deep learning. I am passionate about software refactoring.

My Chinese homepage can be found at here.

Visitors

Since July, 2020

HOBBIES

Badminton

Tennis

Table tennis

Mobile game

Chinese chess

Guitar

Swimming

Traveling

Running

Hiking

Riding

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AWARDS

PROFESSIONAL AWARDS
2018

Awarded by Hebei Educational Department

Three-graded Talent Scientist Award of Hebei Province.
河北省"三三三人才工程"
2018

Awarded by Shijiazhuang Communist Youth League

Shijiazhuang Distinguished Youth Medal Award
石家庄青年五四奖章
2022

Awarded by Hebei Univerity Of Science and Technology

Excellent Teaching Master Award
河北科技大学教学名师
2023

Awarded by China Computer Federation (CCF)

Distinguished Member Award
中国计算机学会杰出会员
2023

Awarded by Hebei Univerity Of Science and Technology

Muxing Scholar Award
牧星学者
PROFESSIONAL POSITIONS
now
2022

Executive Member

CCF Technical Committee on Software Engineering
now
2015

Executive Member

CCF Technical Committee on High Performance Computing
now
2014

Executive Member

CCF Technical Committee on Distributed Computing and System

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PUBLICATIONS

PUBLICATIONS LIST

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ALL
JOURNAL PAPERS
CONFERENCES

SORTING BY DATE

JSEP 2024

Yang Zhang，Shuai Hong

ReInstancer: An Automatic Refactoring Approach for Instanceof Pattern Matching

Journal of Software: Evolution and Process

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Journal

ReInstancer: An Automatic Refactoring Approach for Instanceof Pattern Matching

Yang Zhang，Shuai Hong Journal

Abstract. The \textit{instanceof} pattern matching can improve the code quality and readability by removing the redundant typecasting and simplifying the design in different scenarios. However, existing works do not provide sufficient support for refactoring \textit{instanceof} pattern matching. This paper first identifies several cases that cannot be well handled by existing IDEs. Based on these observations, we propose a novel approach called \textit{ReInstancer} to refactor \textit{instanceof} pattern matching automatically. \textit{ReInstancer} conducts program analysis for multi-branch statements to obtain pattern variables. After analyzing these patterns, the multi-branch statements are optimized and finally refactored into \textit{switch} statements or expressions. \textit{ReInstancer} is evaluated by 20 real-world projects with more than 7700 \textit{instanceof} pattern matching. The experimental results demonstrate that a total of 3558 \textit{instanceof} expressions and 228 multi-branch statements are refactored within 10.8 seconds on average for each project. \textit{ReInstancer} improves the code quality by reducing redundant typecasting, demonstrating its effectiveness.

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IEEE 2024

Dongwen Zhang，Shuai Song，Yang Zhang，Haiyang Liu，Gaojie Shen

Code Smell Detection Research Based on Pre-training and Stacking Models

IEEE Latin America Transactions

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Journal

Code Smell Detection Research Based on Pre-training and Stacking Models

Dongwen Zhang，Shuai Song，Yang Zhang，Haiyang Liu，Gaojie Shen Journal

Abstract. Code smells detection primarily adopts heuristic-based, machine learning, and deep learning approaches. However, to enhance accuracy, most studies employ deep learning methods, but the value of traditional machine learning methods should not be underestimated. Additionally, existing code smells detection methods do not pay sufficient attention to the textual features in the code. To address this issue, this paper proposes a code smell detection method, \textit{SCSmell}, which utilizes static analysis tools to extract structure features, then transforms the code into txt format using static analysis tools, and inputs it into the \textit{BERT} pre-training model to extract textual features. The structure features are combined with the textual features to generate sample data and label code smells instances. The REFCV method is then used to filter important structure features. To deal with the issue of data imbalance, the Borderline-SMOTE method is used to generate positive sample data, and a three-layer Stacking model is ultimately employed to detect code smells. In our experiment, we select 44 large actual projects programs as the training and testing sets and conducted smell detection for four types of code smells: brain class, data class, God class, and brain method. The experimental results indicate that the \textit{SCSmell} method improves the average accuracy by 10.38\% compared to existing detection methods, while maintaining high precision, recall, and F1 scores. The \textit{SCSmell} method is an effective solution for implementing code smells detection.

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NEU
COM 2024

Yang Zhang, Chuyan Ge, Haiyang Liu, Kun Zheng.

Code Smell Detection Based on Supervised Learning Models: A Survey

Neurocomputing

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Journal

Code Smell Detection Based on Supervised Learning Models: A Survey

Yang Zhang,Chuyan Ge, Haiyang Liu, Kun Zheng. Journal

Abstract. Supervised learning-based code smell detection has become one of the dominant approaches to identify code smell. Existing works optimize the process of code smell detection from multiple aspects, such as high-quality dataset, feature selection, and model, etc. Although the accuracy is improved continuously, researchers are confused about what model are the most suitable ones to detect code smell when considering dataset construction and feature selection. Furthermore, existing surveys for code smell mainly analyze the impact of code smell, categorize the concerns of code smell, and repair code smell. There is a lack of systematic analysis and classification of code smell detection based on supervised learning. To this end, we collect 86 papers of code smell detection based on supervised learning ranging from January 2010 to April 2023. A total of 7 research questions is empirically evaluated from different aspects, such as datasets construction, data pre-processing, feature selection, and model training, etc. We conclude that existing works suffer from issues such as sample imbalance, different attention to types of code smell, and limited feature selection. Finally, we suggest possible future research directions.

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ASE 2024

Yang Zhang, Ke Guan, Lining Fang.

MIRROR: Multi-objective Refactoring Recommendation via Correlation Analysis

Automated Software Engineering

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Journal

MIRROR: Multi-objective Refactoring Recommendation via Correlation Analysis

Yang Zhang, Ke Guan, Lining Fang. Journal

Abstract. Refactoring is a critical but complex process to improve code quality by altering software structure without changing the observable behavior. Search-based approaches have been proposed to recommend refactoring solutions. However, existing works tend to leverage all the sub-attributes in an objective and ignore the relationship between the sub-attributes. Furthermore, the types of refactoring operations in the existing works can be further augmented. To this end, this paper proposes a novel approach, called MIRROR, to recommend refactoring by employing a multi-objective optimization across three objectives: (i) improving quality, (ii) removing code smell, and (iii) maximizing the similarity to refactoring history. Unlike previous works, MIRROR provides a way to further optimize attributes in each objective. To be more specific, given an objective, MIRROR investigates the possible correlations among attributes and selects those attributes with low correlations as the representation of this objective. MIRROR is evaluated on 6 real-world projects by answering 6 research questions. The experimental results demonstrate that MIRROR recommends an average of 43 solutions for each project. Furthermore, we compare MIRROR against existing tools JMove and QMove, and show that the F1 of MIRROR is 5.63% and 3.75% higher than that of JMove and QMove, demonstrating the effectiveness of MIRROR.

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ICSE-DEMO 2023

Yang Zhang, Xiaosong Chang, Lining Fang, Yifan Lu

DeepLog: Deep-Learning-Based Log Recommendation.

Proceedings of 45th International Conference on Software Engineering (ICSE - DEMO Track). May 17-20, 2023, Melbourne, Austrilia.

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Conference

DeepLog: Deep-Learning-Based Log Recommendation.

Yang Zhang, Xiaosong Chang, Lining Fang, Yifan Lu Conference

Abstract. Log recommendation plays a vital role in analyzing run-time issues including anomaly detection, performance monitoring, and security evaluation. However, existing deeplearning-based approaches for log recommendation suffer from insufficient features and low F1. To this end, this paper proposes a prototype called DeepLog to recommend log location based on a deep learning model. DeepLog parses the source code into an abstract syntax tree and then converts each method into a block hierarchical tree in which DeepLog extracts both semantic and syntactic features. By doing this, we construct a dataset with more than 110K samples. DeepLog employs a double-branched neural network model to recommend log locations. We evaluate the effectiveness of DeepLog by answering four research questions. The experimental results demonstrate that it can recommend 8,725 logs for 23 projects and the F1 of DeepLog is 28.17% higher than that of the existing approaches, which improves state-of-the-art.

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KBS 2022

Yang Zhang, Chuyan Ge, Shuai Hong, Ruili Tian, Chunhao Dong, Jingjing Liu.

DeleSmell: Code Smell Detection Based on Deep Learning and Latent Semantic Analysis.

Knowledge-Based Systems

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Journal

DeleSmell: Code Smell Detection Based on Deep Learning and Latent Semantic Analysis.

Yang Zhang, Chuyan Ge, Shuai Hong, Ruili Tian, Chunhao Dong, Jingjing Liu. Journal

Abstract. The presence of code smells will increase the risk of failure, make software difficult to maintain, and introduce potential technique debt in the future. Although many deep-learning-based approaches have been proposed to detect code smells, most existing works suffer from the problem of incomplete feature extraction and unbalanced distribution between positive samples and negative samples. Furthermore, the accuracy of existing works can be further improved. This paper proposes a novel approach named DeleSmell to detect code smells based on a deep learning model. The dataset is built by extracting samples from 24 real-world projects. To improve the imbalance in the dataset, a refactoring tool is developed to automatically transform good source code into smelly code and to generate positive samples based on real cases. DeleSmell collects both structural features through iPlasma and semantic features via latent semantic analysis and word2vec. DeleSmell’s model includes a convolutional neural network(CNN) branch and gate recurrent unit(GRU)-attention branch. The final classification is conducted by an support vector machine(SVM). In the experimentation, the effectiveness of DeleSmell is evaluated by answering seven research questions. The experimental results show that DeleSmell improves the accuracy of brain class (BC) and brain method (BM) code smells detection

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CRAD 2022

张杨，乔柳，东春浩, 高鸿斌.

基于深度学习的数据竞争检测方法

计算机研究与发展. 2022, 59(9): 1914-1928.

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Journal

Deep Learning Based Data Race Detection Approach

Yang Zhang, Liu Qiao, Chunhao Dong, Hongbin Gao. Journal

Abstract. Existing approaches for deep-learning-based data race detection are suffering from the issues of single feature extraction and low accuracy. To improve the state-of-the-art, this paper proposes a novel approach called DeleRace to detect data race based on deep learning model. Firstly, DeleRace extracts instruction-level, method-level, and file-level features from a variety of real-world applications. All these features are transformed by word vectorization to build the training dataset. Secondly, ConRacer, as an existing data race tool, is employed to identify the real race. Based on this tool, those positive samples in the training dataset is labelled. To further optimize the dataset, DeleRace leverages SMOTE algorithm to distribute both positive samples and negative ones in balance. Finally, CNN-LSTM model is constructed and a classifier is trained to detect data race. In the experimentation, a total of 26 real-world applications is selected from different fields in Dacapo, JGF, IBM Contest and PJBench benchmark suites. The experimental results show that the accuracy of DeleRace is 96.79% which is 4.65% higher than existing deep-learning-based approaches. Furthermore, the performance of DeleRace is compared with that of both dynamic tools (such as Said and RVPredict) and static tools (such as SRD and ConRacer), which demonstrates the effectiveness of DeleRace.

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NCAA 2022

Yang Zhang, Jiali Yan, Liu Qiao, Hongbin Gao.

A Novel Approach of Data Race Detection Based on CNN-BiLSTM Hybrid Neural Network.

Neural Computing and Applications. 2022.

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Journal

A Novel Approach of Data Race Detection Based on CNN-BiLSTM Hybrid Neural Network.

Yang Zhang, Jiali Yan, Liu Qiao, Hongbin Gao. Journal

Abstract. Existing data race detection approaches based on deep learning are suffering from the problems of unique feature extraction and low accuracy. To this end, this paper proposes a novel approach called SmartRace based on a CNN-BiLSTM hybrid neural network to detect data race. To build the dataset, SmartRace selects 25 real-world applications from five benchmark suites and then extracts multi-level features by a static analysis tool Wala. A tool ConRacer is employed to help label the samples. SmartRace leverages the Kmeans-SMOTE algorithm to make the positive samples and negative counterparts well distributed. The samples are vectorized and fed into a deep learning model. Finally, a CNN-BiLSTM hybrid neural network is built and trained to detect data race. The experimental results show that the accuracy of SmartRace is up to 4.94% higher than that of the existing detection approach. Furthermore, we compare SmartRace with the existing dynamic and static detection tools, demonstrating the effectiveness of SmartRace.

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JOS 2022

张杨, 东春浩, 刘辉, 葛楚妍.

基于预训练模型和多层次信息的代码坏味检测方法

软件学报. 2022, 33(5): 1551-1568

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Journal

Code Smell Detection Approach Based on Pre-Training Model and Multi-Level Information

Yang Zhang, Chunhao Dong, Hui Liu, Chuyan Ge Journal

Abstract. Most of the existing code smell detection approaches rely on code structure information and heuristic rules, which pay little attention to the semantic information embedded in different levels of code, , and the accuracy of code smell detection approaches is not high. To solve this problem, the paper proposes a novel approach DeepSmell based on a pre-trained model and multi-level metics. Firstly, we use the static analysis tool to extract code smell instances and multi-level code metric information in the source program and mark these instances. Secondly, the level information that relate to code smells in the source code are parsed and obtained through the abstract syntax tree. The textual information composed of the level information is combined with code metric information to generate the data set. Finally, text information converts into word vectors using the BERT pre-training model. To apply the GRU-LSTM model to obtain the potential semantic relationship among the identifiers, and combine the CNN model and attention mechanism to code smell detection. The experiment tested four kinds of code smells including feature envy, long method, data class, and god class on 24 open source programs such as JUnit, Xalan, and SPECjbb2005. The experimental results show that DeepSmell improves the average recall and F1 by 9.3% and 10.44% respectively compared with existing detection methods, while maintaining a high level of precision at the same time.

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ICSE-DEMO 2022

Shuai Hong, Yang Zhang, Chaoshuai Li, Yu Bai.

ReInstancer: Automatically Refactoring for Instanceof Pattern Matching.

Proceedings of 44th International Conference on Software Engineering (ICSE - Demo Track). May 22-27, 2022, Pittsburgh, PA, USA.

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Conference

ReInstancer: Automatically Refactoring for Instanceof Pattern Matching.

Shuai Hong, Yang Zhang, Chaoshuai Li, Yu Bai. Conference

Abstract. Pattern matching for instanceof is widely used with the advantage of conditionally extracting components from objects and with the disadvantage of the compulsory usage of type castings. The feature of pattern matching has been periodically previewed in the latest released version of JDK to avoid redundant type castings and to optimize pattern matching in multi-branch statements. Although pattern matching has many benefits, manual refactoring for instanceof pattern matching is time-consuming and tedious. Furthermore, no existing work can provide sufficient support for such refactoring. To this end, this paper presents a refactoring tool ReInstancer that mitigates and simplifies type castings by converting a multi-branch statement with instanceof pattern matching into a switch statement or expression automatically. ReInstancer is evaluated on 7 real-world applications, with a total of 4404 instanceof expressions. The evaluation results demonstrate that ReInstancer can successfully remove 2060 type castings and refactor 141 multibranch statements with a total of 1972 instanceof pattern matching, which improves the code quality.

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JSEP 2021

Yang Zhang, Chunhao Dong.

MARS: Detecting Brain Class/Method Code Smell Based on Metric-Attention Mechanism...

Journal of Software: Evolution and Process. 2021.

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Journal

MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

Yang Zhang, Chunhao Dong. Journal

Abstract. Code smell is the structural design defect that makes programs difficult to understand, maintain, and evolve. Existing works of code smell detection mainly focus on prevalent code smells, such as feature envy, god class, and long method. Few works have been done on detecting brain class/method. Furthermore, existing deep-learning-based approaches leverage the CNN model to improve accuracy by barely increasing the number of layers, which may cause a problem of gradient degradation. To this end, this paper proposes a novel approach called MARS to detect brain class/method. MARS improves the gradient degradation by employing an improved residual network. It increases the weight value of those important code metrics to label smelly samples by introducing a metric–attention mechanism. To support the training of MARS, a dataset called BrainCode is generated by extracting more than 270,000 samples from 20 real-world applications. MARS is evaluated on BrainCode and compared to other machine-learning-based and deep-learning-based approaches. The experimental results demonstrate that the average accuracy of MARS is 2.01 % higher than that of the existing approaches, which improves state-of-the-art.

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JOS 2021

张杨，邵帅，张冬雯.

基于下推自动机的细粒度锁自动重构方法

软件学报. 2021, 32(12): 3710-3717

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Journal

An Automated Refactoring Approach for Fine-grained Lock Based on Pushdown Automaton

Yang Zhang, Shuai Shao, Dongwen Zhang. Journal

Abstract. As coarse-grained locks have a negative impact on the scalability of concurrent programs, this paper proposes an automatic refactoring approach to convert a coarse-grained lock into a fine-grained one. Several static analyses, such as visitor pattern analysis, alias analysis, and side-effect analysis are employed in this approach. The read and write pattern of a critical section is inferred by sideeffect analysis, and thena pushdown automaton is proposed to identify the read and write pattern. Finally, refactoring is conductedbased on these results. We implement an automatic tool FLock as the Eclipse plug-in. Our approach is evaluated by eleven open-source projects including HSQLDB, Jenkins and Cassandra, by presenting results such as the number of refactored locks, changed lines of code, refactoring time, accuracy, program performance after refactoring.We also compareFLock with the existing tools Relocker and CLOCK. The experimental results show that a total of 1757 built-in monitors are refactoredand each refactoring takes an average of 17.5 seconds. The experiments reveal that our tool can help developers convert coarse-grained locks into fine-grained locks effectively.

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ISSTA 2020

Yang Zhang, Shuai Shao, Juan Zhai, Shiqing Ma.

FineLock: Automatically Refactoring Coarse-grained Locks into Fine-grained Locks.

Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA2020). July 18-22, 2020. Virtual Event, USA.

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Conference

FineLock: Automatically Refactoring Coarse-grained Locks into Fine-grained Locks.

Yang Zhang, Shuai Shao, Juan Zhai, Shiqing Ma. Conference

Abstract. Lock is a frequently-used synchronization mechanism to enforce exclusive access to a shared resource. However, lock-based concurrent programs are susceptible to lock contention, which leads to low performance and poor scalability. Furthermore, inappropriate granularity of a lock makes lock contention even worse. Compared to coarse-grained lock, fine-grained lock can mitigate lock contention but difficult to use. Converting coarse-grained lock into fine-grained lock manually is not only error-prone and tedious, but also requires a lot of expertise. In this paper, we propose to leverage program analysis techniques and pushdown automaton to automatically covert coarse-grained locks into fine-grained locks to reduce lock contention. We developed a prototype FineLock and evaluates it on 5 projects. The evaluation results demonstrate FineLock can refactor 1,546 locks in an average of 27.6 seconds, including converting 129 coarse-grained locks into fine-grained locks and 1,417 coarse-grained locks into read/write locks. By automatically providing potential refactoring recommendations, our tool saves a lot of efforts for developers.

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ABOUT

BIO

ABOUT ME

Visitors

Since July, 2020

HOBBIES

AWARDS

Awarded by Hebei Educational Department

Three-graded Talent Scientist Award of Hebei Province.

Awarded by Shijiazhuang Communist Youth League

Shijiazhuang Distinguished Youth Medal Award

Awarded by Hebei Univerity Of Science and Technology

Excellent Teaching Master Award

Awarded by China Computer Federation (CCF)

Distinguished Member Award

Awarded by Hebei Univerity Of Science and Technology

Muxing Scholar Award

Executive Member

CCF Technical Committee on Software Engineering

Executive Member

CCF Technical Committee on High Performance Computing

Executive Member

CCF Technical Committee on Distributed Computing and System

PUBLICATIONS

Yang Zhang，Shuai Hong

ReInstancer: An Automatic Refactoring Approach for Instanceof Pattern Matching

Journal of Software: Evolution and Process

ReInstancer: An Automatic Refactoring Approach for Instanceof Pattern Matching

Dongwen Zhang，Shuai Song，Yang Zhang，Haiyang Liu，Gaojie Shen

Code Smell Detection Research Based on Pre-training and Stacking Models

IEEE Latin America Transactions

Code Smell Detection Research Based on Pre-training and Stacking Models

Yang Zhang, Chuyan Ge, Haiyang Liu, Kun Zheng.

Code Smell Detection Based on Supervised Learning Models: A Survey

Neurocomputing

Code Smell Detection Based on Supervised Learning Models: A Survey

Yang Zhang, Ke Guan, Lining Fang.

MIRROR: Multi-objective Refactoring Recommendation via Correlation Analysis

Automated Software Engineering

MIRROR: Multi-objective Refactoring Recommendation via Correlation Analysis

Yang Zhang, Xiaosong Chang, Lining Fang, Yifan Lu

DeepLog: Deep-Learning-Based Log Recommendation.

Proceedings of 45th International Conference on Software Engineering (ICSE - DEMO Track). May 17-20, 2023, Melbourne, Austrilia.

DeepLog: Deep-Learning-Based Log Recommendation.

Yang Zhang, Chuyan Ge, Shuai Hong, Ruili Tian, Chunhao Dong, Jingjing Liu.

DeleSmell: Code Smell Detection Based on Deep Learning and Latent Semantic Analysis.

Knowledge-Based Systems

DeleSmell: Code Smell Detection Based on Deep Learning and Latent Semantic Analysis.

张杨，乔柳，东春浩, 高鸿斌.

基于深度学习的数据竞争检测方法

计算机研究与发展. 2022, 59(9): 1914-1928.

Deep Learning Based Data Race Detection Approach

Yang Zhang, Jiali Yan, Liu Qiao, Hongbin Gao.

A Novel Approach of Data Race Detection Based on CNN-BiLSTM Hybrid Neural Network.

Neural Computing and Applications. 2022.

A Novel Approach of Data Race Detection Based on CNN-BiLSTM Hybrid Neural Network.

张杨, 东春浩, 刘辉, 葛楚妍.

基于预训练模型和多层次信息的代码坏味检测方法

软件学报. 2022, 33(5): 1551-1568

Code Smell Detection Approach Based on Pre-Training Model and Multi-Level Information

Shuai Hong, Yang Zhang, Chaoshuai Li, Yu Bai.

ReInstancer: Automatically Refactoring for Instanceof Pattern Matching.

Proceedings of 44th International Conference on Software Engineering (ICSE - Demo Track). May 22-27, 2022, Pittsburgh, PA, USA.

ReInstancer: Automatically Refactoring for Instanceof Pattern Matching.

Yang Zhang, Chunhao Dong.

MARS: Detecting Brain Class/Method Code Smell Based on Metric-Attention Mechanism...

Journal of Software: Evolution and Process. 2021.

MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

张杨，邵帅，张冬雯.

基于下推自动机的细粒度锁自动重构方法

软件学报. 2021, 32(12): 3710-3717

An Automated Refactoring Approach for Fine-grained Lock Based on Pushdown Automaton

Yang Zhang, Shuai Shao, Juan Zhai, Shiqing Ma.

FineLock: Automatically Refactoring Coarse-grained Locks into Fine-grained Locks.

Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA2020). July 18-22, 2020. Virtual Event, USA.

FineLock: Automatically Refactoring Coarse-grained Locks into Fine-grained Locks.

BOOKS

Parallel Programming

Concurrency-oriented Software Refactoring

Multicore-oriented Parallel Execution