Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and ...Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging: 1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose iTriage which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches.展开更多
In software development projects,bugs are common phenomena.Developers report bugs in open source repositories.There is a need to develop high quality developer prediction model that considers developer work satisfacti...In software development projects,bugs are common phenomena.Developers report bugs in open source repositories.There is a need to develop high quality developer prediction model that considers developer work satisfaction,keep within limited development cost,and improve bug resolution time.To address and resolve bug report as soon as possible is the main focus of triager when a new bug is reported.Thus,developer work efficiency is an important factor in bug-fixing.To address these issues,a proposed approach recommends a set of developers that could potentially share their knowledge with each other to fix new bug reports.The proposed approach is called developer working efficiency and social network based developer recommendation(DweSn).It is a composite model that builds developers'profile by using developer average bug fixing time,work efficiency to fix variety of bugs,as well as the developer's social interactions with other developers.A similarity measure is applied between new bug and bugs in corpus to extract the list of capable developers from the corpus.The proposed approach only selects those developers who are active and less loaded with work.The developer with the highest profile score is assigned the bugs.We evaluated our approach on the subset of five large open-source projects including Mozilla,Netbeans,Eclipse,Firefox and OpenOffice,and compared it with the state-of-the-art.The results demonstrate that combination of developers'efficiency with their average bug fixing time and interactions in their social network gives good accuracy and efficiently reduces bug tossing length.This approach shows an improvement in prediction accuracy,precision,recall,F-score and reduced bug tossing length up to 93.89%,93.12%,93.46%,93.27%and 93.25%,respectively.The proposed approach achieved a 93%hit ratio and 93.34%mean reciprocal rank,indicating that our proposed triager is able to efficiently assign bugs to correct developers.展开更多
La(Ⅲ) , Y(Ⅲ) complexes with diglycol aldehyde bis-arginine (H2DAAR) and tetraglycol aldehyde bis-lysine (H2TALY) Schiff bases were synthesized. They were characterized and formulated as La( H2DAAR) (NO3)3 ?6H2Oand Y...La(Ⅲ) , Y(Ⅲ) complexes with diglycol aldehyde bis-arginine (H2DAAR) and tetraglycol aldehyde bis-lysine (H2TALY) Schiff bases were synthesized. They were characterized and formulated as La( H2DAAR) (NO3)3 ?6H2Oand Y (H2TALY)(NO3)3 ·5H2Oseparately by elemental analyses. The obtained complexes were investigated in detail by high resolution solid state (HRSS) 13C NMR using cross polarization, magic angle spinning (CPMAS) , and total suppression of sidebands (TOSS)techniques. The results are supported by the liquid state 2D-1 H-13 C COSY NMR spectra. Some new information about the splitting peaks of 13C for -CH=N- group and alkene carbon bands, etc. in HRSS 13C NMR spectra are given.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61690204,61672274,and 61702252, and the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University.
文摘Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging: 1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose iTriage which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches.
文摘In software development projects,bugs are common phenomena.Developers report bugs in open source repositories.There is a need to develop high quality developer prediction model that considers developer work satisfaction,keep within limited development cost,and improve bug resolution time.To address and resolve bug report as soon as possible is the main focus of triager when a new bug is reported.Thus,developer work efficiency is an important factor in bug-fixing.To address these issues,a proposed approach recommends a set of developers that could potentially share their knowledge with each other to fix new bug reports.The proposed approach is called developer working efficiency and social network based developer recommendation(DweSn).It is a composite model that builds developers'profile by using developer average bug fixing time,work efficiency to fix variety of bugs,as well as the developer's social interactions with other developers.A similarity measure is applied between new bug and bugs in corpus to extract the list of capable developers from the corpus.The proposed approach only selects those developers who are active and less loaded with work.The developer with the highest profile score is assigned the bugs.We evaluated our approach on the subset of five large open-source projects including Mozilla,Netbeans,Eclipse,Firefox and OpenOffice,and compared it with the state-of-the-art.The results demonstrate that combination of developers'efficiency with their average bug fixing time and interactions in their social network gives good accuracy and efficiently reduces bug tossing length.This approach shows an improvement in prediction accuracy,precision,recall,F-score and reduced bug tossing length up to 93.89%,93.12%,93.46%,93.27%and 93.25%,respectively.The proposed approach achieved a 93%hit ratio and 93.34%mean reciprocal rank,indicating that our proposed triager is able to efficiently assign bugs to correct developers.
基金Project supported by the National Natural Science Foundation of China (Grant No. 29671026)the Natural Science Foundation of Zhejiang Province (Grant No. 296062)the Laboratory of MRAMP (Grant No. 971502)
文摘La(Ⅲ) , Y(Ⅲ) complexes with diglycol aldehyde bis-arginine (H2DAAR) and tetraglycol aldehyde bis-lysine (H2TALY) Schiff bases were synthesized. They were characterized and formulated as La( H2DAAR) (NO3)3 ?6H2Oand Y (H2TALY)(NO3)3 ·5H2Oseparately by elemental analyses. The obtained complexes were investigated in detail by high resolution solid state (HRSS) 13C NMR using cross polarization, magic angle spinning (CPMAS) , and total suppression of sidebands (TOSS)techniques. The results are supported by the liquid state 2D-1 H-13 C COSY NMR spectra. Some new information about the splitting peaks of 13C for -CH=N- group and alkene carbon bands, etc. in HRSS 13C NMR spectra are given.
