Lizheng头像

Lizheng

Professor

Research direction: Software Engineering

Education:

10 Access

  • Email: lizheng@buct.edu.cn
  • Office : Room 407, Sci.&Tech Building
  • Introduction
  • Research
  • Teaching
  • Funding
  • Publications
  • Awards
  • Honor Reward
  • Admission

Introduction

I am a professor in Department of Computer Science, College of Information Science and Technology at Beijing University of Chemical Techology.


I hold a PhD degree in Computer Science from King's College London, supervised by Mark Harman and Nicolas Gold.


My research interests are software testing in combination with search-based techniques, source code analysis, program slicing and state based model slicing and testing. I orgnized the Chinese Search bases Software Engineering workshops (csbse.org) since 2011.


Visit my profiles at Google, DBLP and ORCID


I serve  IEEE SCAM working conference as Steering Committee member since 2017.


Education

Work Experience

Social Position

Social Activities

Research

  • * Search-based Software Engineering and Testing

    My present work on software engineering includes regression, test case prioritisation, test case generation using genetic, algorithms and empirical investigations of software.


  • * Program Slicing and Merging

    My present work on program slicing concentrates on empirical; studies of new slice-base analyzes and on generating a better; technique by combining program slicing and domain level techniques.


  • * State-based Model Slicing and Testing

    My interest in State-based Model Slicing and Testing has focused on developing algorithms, methods and techniques for scaling down the size and complexity of a model on both theoretical level and empirical level.


Teaching

I am teaching Advanced Software Engineering during 2024-2025. 

Postgraduates

Funding

Test Case Optimization in Continuous Integration based on Deep Reinforcement Learning

2019-01 to 2022-12 | Grant
National Natural Science Foundation of China
GRANT_NUMBER: 61872026

EFSM Slicing and Testing

2015-01 to 2018-12 | Grant
National Natural Science Foundation of China
GRANT_NUMBER: 61472025

GPGPU Based Test Case Prioritization

2012-01 to 2014-12 | Grant
National Natural Science Foundation of China
GRANT_NUMBER: 61170082

Test Generation Based on EFSM Slicing

2010-01 to 2011-12 | Grant
National Natural Science Foundation of China
GRANT_NUMBER: 60903002


Vertical Project

Horizontal Project

Publications

PhD

Supervisors:Prof. Mark Harman andDr. Nicolas Gold
Research group: CREST
Topic: Identifying High-Level Dependence Structures UsingSlice-Based Dependence Analysis

Overview

This thesis presented a framework of the possible combinationof approaches for low-level program slicing-based dependence analysisand high-level concept assignment.  Three combinationtechniques, concept extension, concept abbreviationand concept refinement, are presented and empirical studied toaddress the problem of program maintenance. The ten C subject programsare studied and more than 600 concept bindings areidentified. Dependence based metrics are defined to evaluate threetechniques that provides evidence of both advantages anddisadvantages.


I have passed my viva on 9th February 2009 and submitted thefinal version of my thesis in March 2009.

The thesis has been accepted by ICSM09 DoctoralSymposium.


  • More publications please refer to Google, DBLP and ORCID.

  • 2025

    , , , , , 
    Integrating neural mutation into mutation-based fault localization: A hybrid approach. 
    J. Syst. Softw. 221: 112281 ()
  • , , , , , 
    Boosting mutation-based fault localization by effectively generating Higher-Order Mutants. 
    Inf. Softw. Technol. 180: 107660 ()

  • , , , , , , 
    Prompt Alchemy: Automatic Prompt Refinement for Enhancing Code Generation. IEEE Trans. Software Eng. 51(9): 2472-2493 ()


  • 2024

    , , , , , 
    GBSR: Graph-based suspiciousness refinement for improving fault localization. 
    J. Syst. Softw. 218: 112189 ()
    • , , , , , 
      Delta4Ms: Improving mutation-based fault localization by eliminating mutant bias. 
      Softw. Test. Verification Reliab. 34(4) ()
    • , , , , 
      Predictive Mutation-Based Fault Localization: Balancing Effectiveness and Cost. 
      QRS Companion : 577-582
    • , , , 
      ConDefects: A Complementary Dataset to Address the Data Leakage Concern for LLM-Based Fault Localization and Program Repair. 
      SIGSOFT FSE Companion : 642-646



  • 2023


    VsusFL: Variable-suspiciousness-based Fault Localization for novice programs.
     J. Syst. Softw. 205: 111822 ()

  • A Token-based Compilation Error Categorization and Its Applications. 
    J. Softw. Evol. Process. 35(2) ()

  • , , , , , , :
    Extended Abstract of SeCNN: A semantic CNN parser for code comment generation. 
    SANER : 848-849


  • , , , , , , :
    SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization. 
    COMPSAC : 870-875



  • 2022

    , , , :
    An Empirical Study on Higher-Order Mutation-Based Fault Localization. 
    Int. J. Softw. Eng. Knowl. Eng. 32(1): 1-35 ()
  • , , , , :
    User behavior pattern mining and reuse across similar Android apps. 
    J. Syst. Softw. 183: 111085 ()


  • , , , , , :
    Theoretical Analysis and Empirical Study on the Impact of Coincidental Correct Test Cases in Multiple Fault Localization. 
    IEEE Trans. Reliab. 71(2): 830-849 ()


  • , , , , , , :
    Can Higher-Order Mutants Improve the Performance of Mutation-Based Fault Localization? 
    IEEE Trans. Reliab. 71(2): 1157-1173 ()


Awards

Patent

Honor Reward

Admissions Information