{"title":"基于状态跟踪的RTL验证搜索启发式","authors":"Ziyue Zheng, Yangdi Lyu","doi":"10.23919/DATE56975.2023.10136970","DOIUrl":null,"url":null,"abstract":"Branch coverage is important in the functional val-idation of Register-Transfer-Level (RTL) models. While random tests can cover the majority of easy-to-reach branches, there are still many hard-to-activate branches in today's industrial designs. These remaining corner branches are typically the source of bugs and hardware trojans. Directed test generation approaches using formal methods effectively activate a specific branch but are limited by the state explosion problem. Semi-formal methods, such as concolic testing, improve the scalability by exploring one path at a time. This paper presents a novel concolic testing framework to exercise the corner branches through state tracing-based search heuristics (STSearch). The proposed approach heuristically gen-erates and evaluates input sequences based on a novel heuristic indicator that evaluates the distance between the current state and the target branch condition. The heuristic indicator is designed to utilize both the static structural property of the design and the state from dynamic simulation. Compared to the existing concolic testing approaches, where a full new path is generated in each round by solving path constraints, the cycle-based heuristic search in the proposed approach is more effective and efficient. Experimental results show that our approach significantly outperforms the state-of-the-art approaches in both running time and memory usage.","PeriodicalId":340349,"journal":{"name":"2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"STSearch: State Tracing-based Search Heuristics for RTL Validation\",\"authors\":\"Ziyue Zheng, Yangdi Lyu\",\"doi\":\"10.23919/DATE56975.2023.10136970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Branch coverage is important in the functional val-idation of Register-Transfer-Level (RTL) models. While random tests can cover the majority of easy-to-reach branches, there are still many hard-to-activate branches in today's industrial designs. These remaining corner branches are typically the source of bugs and hardware trojans. Directed test generation approaches using formal methods effectively activate a specific branch but are limited by the state explosion problem. Semi-formal methods, such as concolic testing, improve the scalability by exploring one path at a time. This paper presents a novel concolic testing framework to exercise the corner branches through state tracing-based search heuristics (STSearch). The proposed approach heuristically gen-erates and evaluates input sequences based on a novel heuristic indicator that evaluates the distance between the current state and the target branch condition. The heuristic indicator is designed to utilize both the static structural property of the design and the state from dynamic simulation. Compared to the existing concolic testing approaches, where a full new path is generated in each round by solving path constraints, the cycle-based heuristic search in the proposed approach is more effective and efficient. Experimental results show that our approach significantly outperforms the state-of-the-art approaches in both running time and memory usage.\",\"PeriodicalId\":340349,\"journal\":{\"name\":\"2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/DATE56975.2023.10136970\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/DATE56975.2023.10136970","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
STSearch: State Tracing-based Search Heuristics for RTL Validation
Branch coverage is important in the functional val-idation of Register-Transfer-Level (RTL) models. While random tests can cover the majority of easy-to-reach branches, there are still many hard-to-activate branches in today's industrial designs. These remaining corner branches are typically the source of bugs and hardware trojans. Directed test generation approaches using formal methods effectively activate a specific branch but are limited by the state explosion problem. Semi-formal methods, such as concolic testing, improve the scalability by exploring one path at a time. This paper presents a novel concolic testing framework to exercise the corner branches through state tracing-based search heuristics (STSearch). The proposed approach heuristically gen-erates and evaluates input sequences based on a novel heuristic indicator that evaluates the distance between the current state and the target branch condition. The heuristic indicator is designed to utilize both the static structural property of the design and the state from dynamic simulation. Compared to the existing concolic testing approaches, where a full new path is generated in each round by solving path constraints, the cycle-based heuristic search in the proposed approach is more effective and efficient. Experimental results show that our approach significantly outperforms the state-of-the-art approaches in both running time and memory usage.