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检索条件"任意字段=1st IEEE International Workshop on Automation of Software Test"

共 398 条记录，以下是1-10 订阅

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Evaluating LLMs for Hardware Design and test

Evaluating LLMs for Hardware Design and Test

引用

1st ieee international workshop on LLM-Aided Design (LAD)

作者： Blocklove, Jason Garg, Siddharth Karri, Ramesh Pearce, Hammond New York Univ New York NY 10003 USA Univ New South Wales Sydney Australia

ISBN: (纸本)9798350376098;9798350376081

Large Language Models (LLMs) have demonstrated capabilities for producing code in Hardware Description Languages (HDLs). However, most of the focus remains on their abilities to write functional code, not test code. The hardware design process consists of both design and test, and so eschewing validation and verification leaves considerable potential benefit unexplored, given that a design and test framework may allow for progress towards full automation of the digital design pipeline. In this work, we perform one of the first studies exploring how a LLM can both design and test hardware modules from provided specifications. Using a suite of 8 representative benchmarks, we examined the capabilities and limitations of the state-of-the-art conversational LLMs when producing Verilog for functional and verification purposes. We taped out the benchmarks on a Skywater 130nm shuttle and received the functional chip.

关键词： Hardware Design and Verification CAD LLM

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Machine Learning-based test Case Prioritization using Hyperparameter Optimization 5

Machine Learning-based Test Case Prioritization using Hyperp...

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5th ieee/ACM international Conference on automation of software test (Ast)

作者： Khan, Md Asif Azim, Akramul Liscano, Ramiro Smith, Kevin Tauseef, Qasim Seferi, Gkerta Chang, Yee-Kang Ontario Tech Univ Oshawa ON Canada IBM United Kingdom Ltd Portsmouth Hants England IBM Canada Ltd Markham ON Canada

ISBN: (纸本)9798400705885

Continuous integration pipelines execute extensive automated test suites to validate new software builds. In this fast-paced development environment, delivering timely testing results to developers is critical to ensuring software quality. test case prioritization (TCP) emerges as a pivotal solution, enabling the prioritization of faultprone test cases for immediate attention. Recent advancements in machine learning have showcased promising results in TCP, offering the potential to revolutionize how we optimize testing workflows. Hyperparameter tuning plays a crucial role in enhancing the performance of ML models. However, there needs to be more work investigating the effects of hyperparameter tuning on TCP. Therefore, we explore how optimized hyperparameters influence the performance of various ML classifiers, focusing on the Average Percentage of Faults Detected (APFD) metric. Through empirical analysis of ten real-world, large-scale, diverse datasets, we conduct a grid search-based tuning with 885 hyperparameter combinations for four machine learning models. Our results provide model-specific insights and demonstrate an average 15% improvement in model performance with hyperparameter tuning compared to default settings. We further explain how hyperparameter tuning improves precision (max = 1), recall (max = 0.9633), F1-score (max = 0.9662), and influences APFD value (max = 0.9835), indicating a direct connection between tuning and prioritization performance. Hence, this study underscores the importance of hyperparameter tuning in optimizing failure prediction models and their direct impact on prioritization performance.

关键词： hyperparameter optimization test case prioritization machine learning continuous integration

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Proceedings - 2024 ieee/ACM international Flaky tests workshop, FTW 2024

Proceedings - 2024 IEEE/ACM International Flaky Tests Worksh...

引用

1st international Flaky tests workshop, FTW 2024, co-located with the 46th ACM/ieee international Conference on software Engineering, ICSE 2024

ISBN: (纸本)9798400705588

The proceedings contain 6 papers. The topics discussed include: Presubmit Rescue: automatically ignoring Flakytest executions;regression-test history data for flaky-test research;predicting the lifetime of flaky tests on chrome;on the impact of hitting system resource limits on test flakiness;flaky tests in the AI domain;and can ChatGPT repair non-order-dependent flaky tests?.

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C2HLSC: Can LLMs Bridge the software-to-Hardware Design Gap?

C2HLSC: Can LLMs Bridge the Software-to-Hardware Design Gap?

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1st ieee international workshop on LLM-Aided Design (LAD)

作者： Collini, Luca Garg, Siddharth Karri, Ramesh

ISBN: (纸本)9798350376098;9798350376081

High Level Synthesis (HLS) tools offer rapid hardware design from C code, but their compatibility is limited by code constructs. This paper investigates Large Language Models (LLMs) for refactoring C code into HLS-compatible formats. We present several case studies by using an LLM to rewrite C code for NIst 800-22 randomness tests, a QuickSort algorithm and AES-128 into HLS-synthesizable c. The LLM iteratively transforms the C code guided by user prompts, implementing functions like streaming data and hardware-specific signals. This evaluation demonstrates the LLM's potential to assist hardware design refactoring regular C code into HLS synthesizable C code.

关键词： Chip Design LLM Catapult HLS Cryptocores

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Proceedings - 2020 ieee/ACM 1st international Conference on automation of software test, Ast 2020

Proceedings - 2020 IEEE/ACM 1st International Conference on ...

引用

2020 ieee/ACM 1st international Conference on automation of software test, Ast 2020

ISBN: (纸本)9781450379571

The proceedings contain 17 papers. The topics discussed include: testing apps with real world inputs;automatic ex-vivo regression testing of microservices;a delta-debugging approach to assessing the resilience of actor programs through run-time test perturbations;validating test case migration via mutation analysis;hybrid methods for reducing database schema test suites: experimental insights from computational and human studies;exploratory datamorphic testing of classification applications;blockrace: a big data approach to dynamic block-based data race detection for multithreaded programs;automated analysis of flakiness-mitigating delays;and the power of string solving: simplicity of comparison.

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VerilogReader: LLM-Aided Hardware test Generation

VerilogReader: LLM-Aided Hardware Test Generation

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1st ieee international workshop on LLM-Aided Design (LAD)

作者： Ma, Ruiyang Yang, Yuxin Liu, Ziqian Zhang, Jiaxi Li, Min Huang, Junhua Luo, Guojie Peking Univ Sch Comp Sci Beijing Peoples R China Renmin Univ China Sch Informat Beijing Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China

ISBN: (纸本)9798350376098;9798350376081

test generation has been a critical and labor-intensive process in hardware design verification. Recently, the emergence of Large Language Model (LLM) with their advanced understanding and inference capabilities, has introduced a novel approach. In this work, we investigate the integration of LLM into the Coverage Directed test Generation (CDG) process, where the LLM functions as a Verilog Reader. It accurately grasps the code logic, thereby generating stimuli that can reach unexplored code branches. We compare our framework with random testing, using our self-designed Verilog benchmark suite. Experiments demonstrate that our framework outperforms random testing on designs within the LLM's comprehension scope. Our work also proposes prompt engineering optimizations to augment LLM's understanding scope and accuracy.

关键词： Automatic test Generation LLM Verilog

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Deep Multiple Assertions Generation 1

Deep Multiple Assertions Generation

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ieee/ACM 1st international Conference on AI Foundation Models and software Engineering (FORGE)

作者： Wang, Hailong Xu, Tongtong Wang, Bei Zhejiang Univ Hangzhou Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798400706097

software testing is one of the most crucial parts of the software development life cycle. Developers spend substantial amount of time and efforts on software testing. Recently, there has been a growing scholarly interest in the automation of software testing. However, recent studies have revealed significant limitations in the quality and efficacy of the generated assert statements. These limitations primarily arise due to: (i) the inherent complexity involved in generating assert statements that are both meaningful and effective;(ii) the challenge of capturing the relationship between multiple assertions in a single test case. In recent research, deep learning techniques have been employed to generate meaningful assertions. However, it is typical for a single assertion to be generated for each test case, which contradicts the current situation where over 40% of test cases contain multiple assertions. To address these open challenges, we propose a novel approach, called DeepAssert that exploits the pre-trained model GraphCode-BERT to automatically generate multiple assertions for test methods. It can recommend a sequence of assert statements effectively given a test method and a focal method (the method under test). To evaluate the effectiveness of our approach, we conduct extensive experiments on the dataset built on the top of Methods2test dataset. Experimental results showthat DeepAssert achieves scores of 54.16%, 18.36%, and 15.38% in terms of CodeBLEU, accuracy and perfect prediction and substantially outperforms the state-of-the-art baselines by a large margin. Furthermore, we evaluate the effectiveness of DeepAssert on the task of bug detection and the result indicates that the assert sequences generated by DeepAssert can assist in exposing 42 real-world bugs extracting from Defects4J while only considering the first compiled assert sequence, outperforming the SOTA approaches by a large margin as well.

关键词： software testing Deep Learning Assert statement Generation

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VrT: A CWE-Based Vulnerability Report Tagger : Machine Learning Driven Cybersecurity Tool for Vulnerability Classification 1

VrT: A CWE-Based Vulnerability Report Tagger : Machine Learn...

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1st ieee/ACM international workshop on software Vulnerability, SVM 2023

作者： Alshaya, Fahad A. Alqahtani, Sultan S. Alsamel, Yasser A. Al Imam Mohammad Bin Saud Islamic University Department of Computer Science Riyadh Saudi Arabia

ISBN: (纸本)9798350301908

vulnerability reports play an important role in the software maintenance domain. Disclosing vulnerabilities that attackers can exploit depends on the time of mitigating that software vulnerability. The information on vulnerability reports reported by several security scanning software tools facilitates vulnerability management, trends, and secure software development automation. Tagging of vulnerability reports with vulnerability type has thus far been performed manually. Therefore, human-induced errors and scalability issues suffered due to the shortage of security experts. This paper introduces a tool called Vulnerability Report Tagger (VrT), which leverages machine-learning strategies on vulnerability descriptions for automatically labeling NVD vulnerability reports. VrT automatically predicts the cybersecurity labels to assign to vulnerability text to encourage the use of labeling mechanisms in vulnerability reporting systems to facilitate the vulnerability management and prioritization process. Along with the presentation of the tool architecture and usage, we also evaluate our tool effectiveness in performing the vulnerabilities classification (i.e., tagging) process. Link to the tool: https://***/cz7hwa. © 2023 ieee.

关键词： software design

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MUTCR: test Case Recommendation via Multi-Level Signature Matching 4

MUTCR: Test Case Recommendation via Multi-Level Signature Ma...

引用

4th ieee/ACM international Conference on automation of software test (Ast)

作者： Sun, Weisong Qian, Weidong Luo, Bin Chen, Zhenyu Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China China Ship Sci Res Ctr Wuxi Jiangsu Peoples R China

ISBN: (纸本)9798350324020

Off-the-shelf test cases provide developers with testing knowledge for their reference or reuse, which can help them reduce the effort of creating new test cases. test case recommendation, a major way of achieving test case reuse, has been receiving the attention of researchers. The basic idea behind test case recommendation is that two similar test targets (methods under test) can reuse each other's test cases. However, existing test case recommendation techniques either cannot be used in the cross-project scenario, or have low performance in terms of effectiveness and efficiency. In this paper, we propose a novel test case recommendation technique based on multi-level signature matching. The proposed multi-level signature matching consists of three matching strategies with different strict levels, including level-0 exact matching, level-1 fuzzy matching, and level-2 fuzzy matching. For the query test target given by the developer, level-0 exact matching helps to retrieve exact recommendations (test cases), while level-1 and level-2 fuzzy matching contribute to discovering richer relevant recommendations. We further develop a prototype called MUTCR for test case recommendation. We conduct comprehensive experiments to evaluate the effectiveness and efficiency of MUTCR. The experimental results demonstrate that compared with the state-of-the-art, MUTCR can recommend accurate test cases for more test targets. MUTCR is faster than the best baseline by three times based on the time cost. The user study is also performed to prove that the test cases recommended by MUTCR are useful in practice.

关键词： unit test case test case recommendation test reuse

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An Intelligent Duplicate Bug Report Detection Method Based on Technical Term Extraction 4

An Intelligent Duplicate Bug Report Detection Method Based o...

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4th ieee/ACM international Conference on automation of software test (Ast)

作者： Wu, Xiaoxue Shan, Wenjing Zheng, Wei Chen, Zhiguo Ren, Tao Sun, Xiaobing Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China Northwestern Polytech Univ Sch Software Xian Shaanxi Peoples R China

ISBN: (纸本)9798350324020

As the bug description data generated during the software maintenance cycle, bug reports are usually hastily written by different users, resulting in many redundant and duplicate bug reports (DBRs). Once the DBRs are repeatedly assigned to developers, it will inevitably lead to a serious waste of human resources, especially for large-scale open-source projects. Recently, many experts and scholars have devoted themselves to researching the detection of DBRs and put forward a series of detection methods for DBRs. However, there is still much room for improvement in the performance of DBR prediction. Therefore, this paper proposes a new method for detecting DBR based on technical term extraction, CTEDB (Combination of Term Extraction and DeBERTaV3) for short. This method first extracts technical terms from the text information of bug reports based on Word2Vec and TextRank algorithms. Then it calculates the semantic similarity of technical terms between different bug reports by combining Word2Vec and SBERT models. Finally, it completes the DBR detection task by combining the DeBERTaV3 model. The experimental results show that CTEDB has achieved good results in detecting DBR, and has obviously improved the accuracy, F1-score, recall and precision compared with the baseline approaches.

关键词： duplicate bug reports detection automatic term extraction DeBERTaV3

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