Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-g...Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-grained file level detection cannot accurately locate specific defects.(2)Fine-grained line-level defect prediction methods rely solely on local information of a single line of code,failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line,making it difficult to capture the interaction between global and local information.Therefore,this paper proposes a telecontext-enhanced recursive interactive attention fusion method for line-level defect prediction(TRIA-LineDP).Firstly,using a bidirectional hierarchical attention network to extract semantic features and contextual information from the original code lines as the basis.Then,the extracted contextual information is forwarded to the telecontext capture module to aggregate the global context,thereby enhancing the understanding of broader code dynamics.Finally,a recursive interaction model is used to simulate the interaction between code lines and line-level context,passing information layer by layer to enhance local and global information exchange,thereby achieving accurate defect localization.Experimental results from within-project defect prediction(WPDP)and cross-project defect prediction(CPDP)conducted on nine different projects(encompassing a total of 32 versions)demonstrated that,within the same project,the proposed methods will respectively recall at top 20%of lines of code(Recall@Top20%LOC)and effort at top 20%recall(Effort@Top20%Recall)has increased by 11%–52%and 23%–77%.In different projects,improvements of 9%–60%and 18%–77%have been achieved,which are superior to existing advanced methods and have good detection performance.展开更多
高时空分辨率的风廓线雷达资料在短时降水的临近预报预警中具有重要价值。基于常规气象观测、区域站资料、美国国家环境预报中心(National Center for Environmental Prediction,NCEP)再分析资料及风廓线雷达组网数据,对2023年山东入汛...高时空分辨率的风廓线雷达资料在短时降水的临近预报预警中具有重要价值。基于常规气象观测、区域站资料、美国国家环境预报中心(National Center for Environmental Prediction,NCEP)再分析资料及风廓线雷达组网数据,对2023年山东入汛以来首场大范围暴雨过程进行分析,结果表明,此次过程受高空槽、低涡、低空急流及中尺度切变线共同影响,6月27日为暖区对流,28日以低涡引发的短时强降水为主;强降水主要位于中尺度切变线右侧的正涡度平流区及辐合中心上空,低层辐合与高层辐散的垂直配置为强对流发生提供动力条件;1 km以下超低空风场变化对强降水具有指示意义,低空急流下探及水平风脉动与降水强度呈一定正相关;强降水前1.0 h内低空急流指数与垂直风切变明显增强,降水结束前风切变迅速减弱,近地层出现强切变,风廓线雷达在识别短时强降水临近特征方面具有明显优势。展开更多
基金supported by National Natural Science Foundation of China(no.62376240).
文摘Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-grained file level detection cannot accurately locate specific defects.(2)Fine-grained line-level defect prediction methods rely solely on local information of a single line of code,failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line,making it difficult to capture the interaction between global and local information.Therefore,this paper proposes a telecontext-enhanced recursive interactive attention fusion method for line-level defect prediction(TRIA-LineDP).Firstly,using a bidirectional hierarchical attention network to extract semantic features and contextual information from the original code lines as the basis.Then,the extracted contextual information is forwarded to the telecontext capture module to aggregate the global context,thereby enhancing the understanding of broader code dynamics.Finally,a recursive interaction model is used to simulate the interaction between code lines and line-level context,passing information layer by layer to enhance local and global information exchange,thereby achieving accurate defect localization.Experimental results from within-project defect prediction(WPDP)and cross-project defect prediction(CPDP)conducted on nine different projects(encompassing a total of 32 versions)demonstrated that,within the same project,the proposed methods will respectively recall at top 20%of lines of code(Recall@Top20%LOC)and effort at top 20%recall(Effort@Top20%Recall)has increased by 11%–52%and 23%–77%.In different projects,improvements of 9%–60%and 18%–77%have been achieved,which are superior to existing advanced methods and have good detection performance.
文摘高时空分辨率的风廓线雷达资料在短时降水的临近预报预警中具有重要价值。基于常规气象观测、区域站资料、美国国家环境预报中心(National Center for Environmental Prediction,NCEP)再分析资料及风廓线雷达组网数据,对2023年山东入汛以来首场大范围暴雨过程进行分析,结果表明,此次过程受高空槽、低涡、低空急流及中尺度切变线共同影响,6月27日为暖区对流,28日以低涡引发的短时强降水为主;强降水主要位于中尺度切变线右侧的正涡度平流区及辐合中心上空,低层辐合与高层辐散的垂直配置为强对流发生提供动力条件;1 km以下超低空风场变化对强降水具有指示意义,低空急流下探及水平风脉动与降水强度呈一定正相关;强降水前1.0 h内低空急流指数与垂直风切变明显增强,降水结束前风切变迅速减弱,近地层出现强切变,风廓线雷达在识别短时强降水临近特征方面具有明显优势。
