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An Imbalanced Deep Learning Model for Bug Localization 28

An Imbalanced Deep Learning Model for Bug Localization

引用

28th asia-pacific software engineering conference (APSEC)

作者： Bui Thi Mai Anh Nguyen Viet Luyen Hanoi Univ Sci & Technol Sch Informat & Commun Technol Lab Intelligent Software Engn Hanoi Vietnam

ISBN: (纸本)9781665438131

Debugging and locating faulty source files are tedious and time-consuming tasks. To improve the productivity and to help developers focus on crucial files, automated bug localization models have been proposed for years. These models recommend buggy source files by ranking them according to their relevance to a given bug report. There are two significant challenges in this research field: (i) narrowing the lexical gap between bug reports which are typically described using natural languages and source files written in programming languages;(ii) reducing the impact of imbalanced data distribution in model training as a far fewer of source files relate to a given bug report while the majority of them are not relevant. In this paper, we propose a deep neural network model to investigate essential information hidden within bug reports and source files through capturing not only lexical relations but also semantic details as well as domain knowledge features such as historical bug fixings, code change history. To address the skewed class distribution, we apply a focal loss function combining with a bootstrapping method to rectify samples of the minority class within iterative training batches to our proposed model. We assessed the performance of our approach over six large scale Java open-source projects. The empirical results have showed that the proposed method outperformed other state-of-the-art models by improving the Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) scores from 3% to 11% and from 2% to 14%, respectively.

关键词： bug localization deep neural network imbalanced data-set bootstrapping

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Categorizing npm Packages by Analyzing the Text Information in software Repositories 28

Categorizing npm Packages by Analyzing the Text Information ...

引用

28th asia-pacific software engineering conference (APSEC)

作者： Wang, Yu Liu, Huaxiao Gao, Shanquan Li, Shujia Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun Peoples R China

ISBN: (纸本)9781665437844

To prevent JavaScript developers from reinventing wheels, npm ecosystem provides numerous third-party libraries for developers to realize relevant functionalities. Npm displays the tags provided by the creators for these packages to help developers find suitable ones. However, not all creators have the habit of tagging their packages, and thus npm cannot provide tag information of a lot of packages for developers to help them understand the package functionalities effectively. Considering that many tags are unrelated to the functionality of packages, we propose a method to find out the tags that are important to distinguish the functionality categories of packages and assign them to untagged packages for assisting developers in the process of retrieving the packages. Firstly, we analyze the attribute of existing tags in npm to establish category tags (functionality categories). Then, we further mine the readme of tagged packages to generate keywords for each category tag. Finally, our method identifies category tags for untagged packages by measuring the similarity between their readme and the keywords of category tags. The evaluation demonstrates that our approach has a good performance in assigning category tags to untagged packages.

关键词： Tag Multi-label Classification JavaScript npm

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A Novel Architectural Design for Solving Lost-Link Problems in UAV Collaboration 28

A Novel Architectural Design for Solving Lost-Link Problems ...

引用

28th asia-pacific software engineering conference (APSEC)

作者： Airlangga, Gregorius Liu, Alan Atma Jaya Catholic Univ Indonesia Informat Syst Jakarta Indonesia Natl Chung Cheng Univ Elect Engn Chiayi Taiwan

ISBN: (纸本)9781665437844

Research in unmanned aerial vehicles (UAVs) has gained attention from various communities because of their potential usage in improving safety and efficiency in different applications. An UAV has shown promising results in dangerous conditions such as forest fires, search and rescue, medical deliveries, wildlife monitoring and geophysical scanning. Some external conditions like slow or no internet connection areas such as rural, farm, forest, ocean, etc. may affect the performance of the UAVs. These conditions can be considered as lost-link problems. Several approaches have been conducted to resolve such issues by implementing robust on-board architecture, machine learning approaches and developing knowledge based reasoning systems. However, much of software architecture research has concentrated on UAV implementation in normal network condition. Thus, we propose a model for considering lost-link problems in software architecture. In this paper, we describe two interconnected architectures for client and server. The UAV as a client is controlled by microkernel based architecture and the server is developed using microservice architecture. Both of them are connected using a synchronizer component to collect, filter, analyze, predict, and mitigate an UAV when a lost-link problem occurs. Therefore, the UAV can still find an appropriate action to complete a mission as far as the sensor and actuator are not in a critical condition. Experiment results show that our approach yields high percentage of mission accomplishment, fault tolerance and performance in a lost-link situation.

关键词： UAV Microkernel Microservice software Architecture Design Patterns

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Research on Image Detection and Coordinate Reading of 2D Machining Path

Research on Image Detection and Coordinate Reading of 2D Mac...

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2022 IEEE asia-pacific conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Chu, Yingnan Li, Hui Tianjin Vocational and Technical Normal University School of Automation and Electrical Engineering Tianjin China

ISBN: (纸本)9781665409025

Traditional image recognition technology is easily affected by environmental factors, has poor anti-noise ability, low image recognition accuracy, low efficiency and poor application flexibility. In actual industrial production, image recognition technology has a wide range of applications. Therefore, this paper aims to optimize traditional image recognition technology, apply MATLAB software to process images to obtain path information, and provide path information for driving devices more accurately, stably and quickly. © 2022 IEEE.

关键词： MATLAB

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A Snapshot of software Testing Education and Instructional Interventions

A Snapshot of Software Testing Education and Instructional I...

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asia-pacific conference on software engineering

作者： Nasir Mehmood Minhas Ayodele A. Barrett School of Innovation Design and Engineering Malardalen University Västerås Sweden

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

This study assesses software testing education in Europe and the Americas through a literature review, dividing selected studies into two categories: 1) the state of software testing education and 2) empirical interventions in universities. The review reveals a limited number of studies, primarily from European and American institutions, and highlights the scarcity of dedicated software testing courses. Although interventions, such as problem-based learning and gamification, have demonstrated improved student engagement and performance, challenges related to time restrictions, instructors' skills, and content management restrict the introduction of such interventions. The study underscores the need for dedicated software testing courses at both undergraduate and master's levels, as these can address issues of time and content management, enable instructors to refine their skills, and better prepare students for industry demands.

关键词： software testing Training South America Content management Europe Time factors Online services North America software engineering Systematic literature review

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Automated Feedback Generation for Multiple Function Programs 28

Automated Feedback Generation for Multiple Function Programs

引用

28th asia-pacific software engineering conference (APSEC)

作者： Choi, Dongwook Heo, Jinseok Lee, Eunseok Sungkyunkwan Univ Dept Software Engn Suwon South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Sungkyunkwan Univ Dept Comp Sci & Engn Suwon South Korea

ISBN: (纸本)9781665437844

Automated Feedback Generation (AFG) was proposed to automatically generate personalized feedback on students' programming assignments. Existing AFG techniques have been developed mainly for novice programmers, so feedback on complex programs cannot be generated. Therefore, we propose MUNCK, which automatically generates feedback for multiple function programs, one of the complex programs. Our experiment shows that MUNCK can generate feedback for 90% of multiple function programs.

关键词： Automated Feedback Generation Programming Assignments Program Standardization Automatic Program Repair

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NeoMycelia: A software reference architecturefor big data systems 28

NeoMycelia: A software reference architecturefor big data sy...

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28th asia-pacific software engineering conference (APSEC)

作者： Ataei, Pouya Litchfield, Alan Auckland Univ Technol Sch Engn Comp & Math Sci Auckland New Zealand Auckland Univ Technol Serv & Cloud Comp Res Lab Auckland New Zealand

ISBN: (纸本)9781665437844

The big data revolution began when the volume, velocity, and variety of data completely overwhelmed the systems used to store, manipulate and analyze that data. As a result, a new class of software systems emerged called big data systems. While many attempted to harness the power of these new systems, it is estimated that approximately 75% of the big data projects have failed within the last decade. One of the root causes of this is software engineering and architecture aspect of these systems. This paper aims to facilitate big data system development by introducing a software reference architecture. The work provides an event driven microservices architecture that addresses specific limitations in current big data reference architectures (RA). The artefact development has followed the principles of empirically grounded RAs. The RA has been evaluated by developing a prototype that solves a real-world problem in practice. At the end, succesful implementation of the reference architecture have been presented. The results displayed a good degree of applicability with respect to quality factors.

关键词： Reference architecture Architecture Big data reference architecture Big data architecture Big data systems Big data software engineering Event driven Microservices

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ExGen: Ready-To-Use Exercise Generation in Introductory Programming Courses 31

ExGen: Ready-To-Use Exercise Generation in Introductory Prog...

引用

31st International conference on Computers in Education (ICCE)

作者： Ta, Nguyen Binh Duong Nguyen, Hua Gia Phuc Gottipati, Swapna Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore

ISBN: (纸本)9786269689019

In introductory programming courses, students as novice programmers would benefit from doing frequent practices set at a difficulty level and concept suitable for their skills and knowledge. However, setting many good programming exercises for individual learners is very time-consuming for instructors. In this work, we propose an automated exercise generation system, named ExGen, which leverages recent advances in pre-trained large language models (LLMs) to automatically create customized and ready-to-use programming exercises for individual students on-demand. The system integrates seamlessly with Visual Studio Code, a popular development environment for computing students and software engineers. ExGen effectively does the following: 1) maintaining a set of seed exercises in a personalized database stored locally for each student;2) constructing appropriate prompts from the seed exercises to be sent to a cloud-based LLM deployment for generating candidate exercises;and 3) implementing a novel combination of filtering checks to automatically select only ready-to-use exercises for a student to work on. Extensive evaluation using more than 600 Python exercises demonstrates the effectiveness of ExGen in generating customized, ready-to-use programming exercises for new computing students.

关键词： introductory programming courses exercise generation large language models prompt engineering auto-filtering

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Prompt Learning for Developing software Exploits 23

Prompt Learning for Developing Software Exploits

引用

14th asia-pacific Symposium on Internetware, Internetware 2023

作者： Ruan, Xiaoming Yu, Yaoxiang Ma, Wenhao Cai, Bo Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9798400708947

A software exploit is a sequence of commands that exploits software vulnerabilities or security flaws, written either by security researchers as a Proof-Of-Concept (POC) threat or by malicious attackers for use in their operations. Writing exploits is a challenging task since it is time-consuming and costly. Pre-trained Language Models (PLMs) for code can benefit automatic exploit generation, achieving state-of-the-art performance. However, the typical paradigm for using these PLMs is fine-tuning, which leads to a significant gap between the language model pre-training and the target task fine-tuning process since they are in different forms. In this paper, we propose a prompt learning approach PT4Exploits based on the PLM, i.e., CodeT5 to automatically generate desired exploits by modifying the original English description inputs. More specifically, PT4Exploits can better elicit knowledge from the PLM by inserting trainable prompt tokens into the original input to construct the same form as the pre-training tasks. Experimental results show that our approach can significantly outperform baseline models on both automatic evaluation and human evaluation. Additionally, we conduct extensive experiments to investigate the performance of prompt learning on few-shot settings and the scenario of different prompt templates, which also show the competitive effectiveness of PT4Exploits. © 2023 ACM.

关键词： Learning systems

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Structural-semantics Guided Program Simplification for Understanding Neural Code Intelligence Models 23

Structural-semantics Guided Program Simplification for Under...

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14th asia-pacific Symposium on Internetware, Internetware 2023

作者： Shi, Chaoxuan Zhu, Tingwei Zhang, Tian Pang, Jun Pan, Minxue State Key Lab for Novel Software Technology Nanjing University Nanjing Jiangsu China Fstm and SnT University of Luxembourg Esch-sur-Alzette Luxembourg

ISBN: (纸本)9798400708947

Neural code intelligence models are cutting-edge automated code understanding technologies that have achieved remarkable performance in various software engineering tasks. However, the lack of deep learning models' interpretability hinders the application of deep learning based code intelligence models in real-world scenarios, particularly in security-critical domains. Previous studies use program simplification to understand neural code intelligence models, but they have overlooked the fact that the most significant difference between source code and natural language is the code's structural semantics. In this paper, we first conduct an empirical study to identify the critical code structural semantic features valued by neural code intelligence models, and then we propose a novel program simplification method called SSGPS (Structural-Semantics Guided Program Simplification). Results on three code summarization models show that SSGPS can reduce training and testing time by 20-40% while controlling the decrease in model performance by less than 4%, demonstrating that our method can retain the critical code structural semantics for understanding neural code intelligence models. © 2023 ACM.

关键词： software engineering

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