Loading [MathJax]/extensions/MathMenu.js
Muhammad Usman Ghani - IEEE Xplore Author Profile

Scheduled Maintenance: On Tuesday, May 20, IEEE Xplore will undergo scheduled maintenance from 1:00-5:00 PM ET (6:00-10:00 PM UTC). During this time, there may be intermittent impact on performance. We apologize for any inconvenience.

Subscribe
ADVANCED SEARCH

Author details

< Back

Muhammad Usman Ghani

Publications
6
Citations
153
Publications by Year
20182021
Co-Authors:
Show All Co-Authors (7)
Image of author Muhammad Usman Ghani

Muhammad Usman Ghani

Affiliation

Department of Electrical and Computer Engineering
Boston University
Boston, MA, USA

Publication Topics

Biography

Muhammad Usman Ghani received the B.S. degree in electrical engineering from COMSATS Institute of Information Technology, Islamabad, Pakistan, in 2013, the M.S. degree in computer science and engineering from Sabanci University, Istanbul, Turkey, in 2016 and the Ph.D. degree in electrical engineering from the Boston University, Boston, MA, USA, in 2021. He is currently a Research Scientist with Philips Research North America, Cambridge, MA, USA. His research interests include deep learning, computational imaging, computer vision, and biomedical image processing.(Based on document published on 2 March 2021).
Publications
6
Citations
153
Publications by Year
20182021
Co-Authors:
Show All Co-Authors (7)
Author's Published Works

Showing 1-10 of 10 results

  • Sort

Filter Results

Show

Sort By

Results

Evaluating Large Language Models in Class-Level Code Generation

Xueying Du;Mingwei Liu;Kaixin Wang;Hanlin Wang;Junwei Liu;Yixuan Chen;Jiayi Feng;Chaofeng Sha;Xin Peng;Yiling Lou

2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE)
Year: 2024 | Conference Paper |
Cited by: Papers (8)
Recently, many large language models (LLMs) have been proposed, showing advanced proficiency in code generation. Meanwhile, many efforts have been dedicated to evaluating LLMs on code generation benchmarks such as HumanEval. Although being very helpful for comparing different LLMs, existing evaluation focuses on a sim-ple code generation scenario (i.e., function-level or statement-level code gener...Show More

Evaluating Large Language Models in Class-Level Code Generation

Xueying Du;Mingwei Liu;Kaixin Wang;Hanlin Wang;Junwei Liu;Yixuan Chen;Jiayi Feng;Chaofeng Sha;Xin Peng;Yiling Lou

Year: 2024 | Conference Paper |

Knowledge Graph based Explainable Question Retrieval for Programming Tasks

Mingwei Liu;Simin Yu;Xin Peng;Xueying Du;Tianyong Yang;Huanjun Xu;Gaoyang Zhang

2023 IEEE International Conference on Software Maintenance and Evolution (ICSME)
Year: 2023 | Conference Paper |
Developers often seek solutions for their programming problems by retrieving existing questions on technical Q&A sites such as Stack Overflow. In many cases, they fail to find relevant questions due to the knowledge gap between the questions and the queries or feel it hard to choose the desired questions from the returned results due to the lack of explanations about the relevance. In this paper, ...Show More

Knowledge Graph based Explainable Question Retrieval for Programming Tasks

Mingwei Liu;Simin Yu;Xin Peng;Xueying Du;Tianyong Yang;Huanjun Xu;Gaoyang Zhang

Year: 2023 | Conference Paper |

CodeGen4Libs: A Two-Stage Approach for Library-Oriented Code Generation

Mingwei Liu;Tianyong Yang;Yiling Lou;Xueying Du;Ying Wang;Xin Peng

2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
Automated code generation has been extensively studied in recent literature. In this work, we first survey 66 participants to motivate a more pragmatic code generation scenario, i.e., library-oriented code generation, where the generated code should implement the functionally of the natural language query with the given library. We then revisit existing learning-based code generation techniques an...Show More

CodeGen4Libs: A Two-Stage Approach for Library-Oriented Code Generation

Mingwei Liu;Tianyong Yang;Yiling Lou;Xueying Du;Ying Wang;Xin Peng

Year: 2023 | Conference Paper |

Task-Oriented ML/DL Library Recommendation Based on a Knowledge Graph

Mingwei Liu;Chengyuan Zhao;Xin Peng;Simin Yu;Haofen Wang;Chaofeng Sha

IEEE Transactions on Software Engineering
Year: 2023 | Volume: 49, Issue: 8 | Journal Article |
Cited by: Papers (5)
AI applications often use ML/DL (Machine Learning/Deep Learning) models to implement specific AI tasks. As application developers usually are not AI experts, they often choose to integrate existing implementations of ML/DL models as libraries for their AI tasks. As an active research area, AI attracts many researchers and produces a lot of papers every year. Many of the papers propose ML/DL models...Show More

Task-Oriented ML/DL Library Recommendation Based on a Knowledge Graph

Mingwei Liu;Chengyuan Zhao;Xin Peng;Simin Yu;Haofen Wang;Chaofeng Sha

Year: 2023 | Volume: 49, Issue: 8 | Journal Article |

API-Related Developer Information Needs in Stack Overflow

Mingwei Liu;Xin Peng;Andrian Marcus;Shuangshuang Xing;Christoph Treude;Chengyuan Zhao

IEEE Transactions on Software Engineering
Year: 2022 | Volume: 48, Issue: 11 | Journal Article |
Cited by: Papers (13)
Stack Overflow (SO) provides informal documentation for APIs in response to questions that express API related developer needs. Navigating the information available on SO and getting information related to a particular API and need is challenging due to the vast amount of questions and answers and the tag-driven structure of SO. In this paper we focus on identifying and classifying fine-grained de...Show More

API-Related Developer Information Needs in Stack Overflow

Mingwei Liu;Xin Peng;Andrian Marcus;Shuangshuang Xing;Christoph Treude;Chengyuan Zhao

Year: 2022 | Volume: 48, Issue: 11 | Journal Article |

Generating Concept based API Element Comparison Using a Knowledge Graph

Yang Liu;Mingwei Liu;Xin Peng;Christoph Treude;Zhenchang Xing;Xiaoxin Zhang

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Developers are concerned with the comparison of similar APIs in terms of their commonalities and (often subtle) differences. Our empirical study of Stack Overflow questions and API documentation confirms that API comparison questions are common and can often be answered by knowledge contained in API reference documentation. Our study also identifies eight types of API statements that are useful fo...Show More

Generating Concept based API Element Comparison Using a Knowledge Graph

Yang Liu;Mingwei Liu;Xin Peng;Christoph Treude;Zhenchang Xing;Xiaoxin Zhang

Year: 2020 | Conference Paper |

Learning based and Context Aware Non-Informative Comment Detection

Mingwei Liu;Yanjun Yang;Xin Peng;Chong Wang;Chengyuan Zhao;Xin Wang;Shuangshuang Xing

2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)
Year: 2020 | Conference Paper |
This report introduces the approach that we have designed and implemented for the DeClutter challenge of Doc-Gen2, which detects non-informative code comments. The approach combines both comment based text classification and code context based prediction. Based on the approach, our "fduse" team achieved the best F1 score (0.847) in the competition.Show More

Learning based and Context Aware Non-Informative Comment Detection

Mingwei Liu;Yanjun Yang;Xin Peng;Chong Wang;Chengyuan Zhao;Xin Wang;Shuangshuang Xing

Year: 2020 | Conference Paper |

Source Code based On-demand Class Documentation Generation

Mingwei Liu;Xin Peng;Xiujie Meng;Huanjun Xu;Shuangshuang Xing;Xin Wang;Yang Liu;Gang Lv

2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
In this paper, we present OpenAPIDocGen2, a tool that generates on-demand class documentation based on source code and documentation analysis. For a given class, OpenAPIDocGen2 generates a combined documentation for it, which includes functionality descriptions, directives, domain concepts, usage examples, class/method roles, key methods, relevant classes/methods, characteristics and concepts clas...Show More

Source Code based On-demand Class Documentation Generation

Mingwei Liu;Xin Peng;Xiujie Meng;Huanjun Xu;Shuangshuang Xing;Xin Wang;Yang Liu;Gang Lv

Year: 2020 | Conference Paper |

Automatic Generation of API Documentations for Open-Source Projects

Xin Peng;Yifan Zhao;Mingwei Liu;Fengyi Zhang;Yang Liu;Xin Wang;Zhenchang Xing

2018 IEEE Third International Workshop on Dynamic Software Documentation (DySDoc3)
Year: 2018 | Conference Paper |
Cited by: Papers (3)
Open-source projects often have only incomplete and insufficient API documentations. To improve the efficiency of development and ensure the correctness of API usage, it is desired that the developers can be supported with automatically gener- ated documentation based on a combination of knowledge from different sources. In this paper, we describe OpenAPIDocGen, a system that can automatically gen...Show More

Automatic Generation of API Documentations for Open-Source Projects

Xin Peng;Yifan Zhao;Mingwei Liu;Fengyi Zhang;Yang Liu;Xin Wang;Zhenchang Xing

Year: 2018 | Conference Paper |

Improving API Caveats Accessibility by Mining API Caveats Knowledge Graph

Hongwei Li;Sirui Li;Jiamou Sun;Zhenchang Xing;Xin Peng;Mingwei Liu;Xuejiao Zhao

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)
Year: 2018 | Conference Paper |
Cited by: Papers (67)
API documentation provides important knowledge about the functionality and usage of APIs. In this paper, we focus on API caveats that developers should be aware of in order to avoid unintended use of an API. Our formative study of Stack Overflow questions suggests that API caveats are often scattered in multiple API documents, and are buried in lengthy textual descriptions. These characteristics m...Show More

Improving API Caveats Accessibility by Mining API Caveats Knowledge Graph

Hongwei Li;Sirui Li;Jiamou Sun;Zhenchang Xing;Xin Peng;Mingwei Liu;Xuejiao Zhao

Year: 2018 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.