Staygreen syndrome or Zhengqing in soybean has recently become a major issue for Chinese growers in the Huang-Huai-Hai river basin.Although previous studies revealed that staygreen can be induced when pods/seeds are d...Staygreen syndrome or Zhengqing in soybean has recently become a major issue for Chinese growers in the Huang-Huai-Hai river basin.Although previous studies revealed that staygreen can be induced when pods/seeds are damaged, it is unknown whether virus infection or insect infestation causes staygreen.To determine whether viral infection causes staygreen, a survey of soybean staygreen incidence in the Huang-Huai-Hai river basin was conducted in 2016 and 2017.Diseased samples were collected and analyzed using DAS-ELISA for Soybean mosaic virus, Watermelon mosaic virus, Bean pod mottle virus, Cucumber mosaic virus, and Bean common mosaic virus.The survey showed that the severity of soybean staygreen syndrome was most prevalent in Beijing, Henan, Shaanxi, and some parts of Shandong provinces, with yield losses from 0 to nearly 100%, but only a small fraction of samples were positive for the tested viruses.A field cage experiment and an insecticide treatment field trial were conducted to determine the contribution of the bean bug, Riptortus pedestris, to staygreen incidence.The field cage experiment showed that R.pedestris treatment resulted in shorter plants, more empty pods, increased numbers of abnormal seeds, and decreased yields.The field experiment showed that there were fewer R.pedestris and less soybean staygreen incidence in fields treated with insecticide than in untreated control fields.Together, these results suggest that R.pedestris infestation rather than virus infection induces staygreen syndrome and that growers in this region can mitigate staygreen syndrome via bean bug control.展开更多
A 2-yr field study was conducted to assess the effects of transgenic japonica rice（KMD1 and KMD2） with a synthetic cry1 Ab gene from Bacillus thuingiensis Berliner on population dynamics and seasonal average densiti...A 2-yr field study was conducted to assess the effects of transgenic japonica rice（KMD1 and KMD2） with a synthetic cry1 Ab gene from Bacillus thuingiensis Berliner on population dynamics and seasonal average densities of five thrips species including Stenchaetothrips biformis（Bagnall）,Frankliniella intonsa（Trybom）,F.tenuicornis（Uzel）,Haplothrips aculeatus（Fabricius）,Haplothrips tritici（Kurd） and their general predatory flower bug,Orius similis Zheng as compared to the parental control rice line using the white,blue and yellow sticky card traps.Population dynamics and seasonal average densities of these five thrips species and their general predatory flower bug were not significantly affected by rice type.Additionally,the white sticky card trap was suggested to be the most suitable for monitoring the population of these five thrips species and their general predator.These results show that our tested Bt rice lines do not interrupt the population of non-target thrips species and their general predatory flower bug in the field,and also cannot result in more occurrence of these thrips species in the rice ecosystem.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
In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they oft...In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects （i.e., surprise bugs）, or break pre-existing functionalities and destroy the user experience （i.e., breakage bugs）. Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE （synthetic minority over-sampling technique） ＋ KNN （K-nearest neighbours） for surprise bug identification and RUS （random under-sampling） ＋ NB （naive Bayes） for breakage bug identification outperform the Fl-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.展开更多
The quality of a software system is partially determined by its structure（topological structure）,so the need to quantitatively analyze the quality of the structure has become eminent.In this paper a novel metric cal...The quality of a software system is partially determined by its structure（topological structure）,so the need to quantitatively analyze the quality of the structure has become eminent.In this paper a novel metric called software quality of structure（SQoS） is presented for quantitatively measuring the structural quality of object-oriented（OO） softwares via bug propagation analysis on weighted software networks（WSNs）.First,the software systems are modeled as a WSN,weighted class dependency network（WCDN）,in which classes are nodes and the interaction between every pair of classes if any is a directed edge with a weight indicating the probability that a bug in one class will propagate to the other.Then we analyze the bug propagation process in the WCDN together with the bug proneness of each class,and based on this,a metric（SQoS） to measure the structural quality of OO softwares as a whole is developed.The approach is evaluated in two case studies on open source Java programs using different software structures（one employs design patterns and the other does not） for the same OO software.The results of the case studies validate the effectiveness of the proposed metric.The approach is fully automated by a tool written in Java.展开更多
Memory leaks are a common type of defect that is hard to detect manually. Existing memory leak detection tools suffer from lack of precise interprocedural analysis and path-sensitivity. To address this problem, we pre...Memory leaks are a common type of defect that is hard to detect manually. Existing memory leak detection tools suffer from lack of precise interprocedural analysis and path-sensitivity. To address this problem, we present a static interprocedural analysis algorithm, that performs fully pathsensitive analysis and captures precise function behaviors, to detect memory leak in C programs. The proposed algorithm uses path-sensitive symbolic execution to track memory actions in different program paths guarded by path conditions. A novel analysis model called memory state transition graph （MSTG） is proposed to describe the tracking process and its results. In order to do interprocedural analysis, the proposed algorithm generates a summary for each procedure from MSTG and applies the summary at the procedure＇s call sites. A prototype tool called Melton is implemented for this procedure. Melton was applied to five open source C programs and 41 leaks were found. More than 90% of these leaks were subsequently confirmed and fixed by their maintainers. For comparison with other tools, Melton was also applied to some programs in standard performance evaluation corporation （SPEC） CPU 2000 benchmark suite and detected more leaks than the state of the art approaches.展开更多
The large number of new bug reports received in bug repositories of software systems makes their management a challenging task.Handling these reports manually is time consuming,and often results in delaying the resolu...The large number of new bug reports received in bug repositories of software systems makes their management a challenging task.Handling these reports manually is time consuming,and often results in delaying the resolution of important bugs.To address this issue,a recommender may be developed which automatically prioritizes the new bug reports.In this paper,we propose and evaluate a classification based approach to build such a recommender.We use the Na¨ ve Bayes and Support Vector Machine （SVM） classifiers,and present a comparison to evaluate which classifier performs better in terms of accuracy.Since a bug report contains both categorical and text features,another evaluation we perform is to determine the combination of features that better determines the priority of a bug.To evaluate the bug priority recommender,we use precision and recall measures and also propose two new measures,Nearest False Negatives （NFN） and Nearest False Positives （NFP）,which provide insight into the results produced by precision and recall.Our findings are that the results of SVM are better than the Na¨ ve Bayes algorithm for text features,whereas for categorical features,Na¨ ve Bayes performance is better than SVM.The highest accuracy is achieved with SVM when categorical and text features are combined for training.展开更多
Since its outbreak in December 2019 in Wuhan Province (China), the Coronavirus (COVID-19) disease quickly spread around the world in such a way that most response plans were outdated. There was an urgent need to chang...Since its outbreak in December 2019 in Wuhan Province (China), the Coronavirus (COVID-19) disease quickly spread around the world in such a way that most response plans were outdated. There was an urgent need to change and adapt response strategies as the virus globally spread. Entire firms and economies were brought to a standstill in order to reduce the virus’ capacity to spread and to limit some of the short-term impacts in order to save time and find out solutions to come back to a more or less normal way of life. Thus, most of the countries that closed their air, sea and land borders had to reopen them progressively, with travel restrictions submitted to rigid controls. In Côte d’Ivoire, as in all other countries, air travellers leaving the territory were required to provide a certificate for a negative COVID-19 test, valid for 24 to 72 hours depending on the country of destination. However, the national system implemented could not provide a result before 48 hours. The objective of this work was to develop an alternative strategy to the system for air travellers who were in a hurry and those who had a computer bug in obtaining their result. A total of 38,444 air travellers benefited from this strategy implemented by the Institut Pasteur de Côte d’Ivoire during these two years.展开更多
The existing software bug localization models treat the source file as natural language, which leads to the loss of syntactical and structure information of the source file. A bug localization model based on syntactic...The existing software bug localization models treat the source file as natural language, which leads to the loss of syntactical and structure information of the source file. A bug localization model based on syntactical and semantic information of source code is proposed. Firstly, abstract syntax tree(AST) is divided based on node category to obtain statement sequence. The statement tree is encoded into vectors to capture lexical and syntactical knowledge at the statement level.Secondly, the source code is transformed into vector representation by the sequence naturalness of the statement. Therefore,the problem of gradient vanishing and explosion caused by a large AST size is obviated when using AST to the represent source code. Finally, the correlation between bug reports and source files are comprehensively analyzed from three aspects of syntax, semantics and text to locate the buggy code. Experiments show that compared with other standard models, the proposed model improves the performance of bug localization, and it has good advantages in mean reciprocal rank(MRR), mean average precision(MAP) and Top N Rank.展开更多
基金supported by the National Key Research and Development Program of China (2017YFD0101400) to T.HanChina Agriculture Research System (CARS-04) to T.Han and K.Li
文摘Staygreen syndrome or Zhengqing in soybean has recently become a major issue for Chinese growers in the Huang-Huai-Hai river basin.Although previous studies revealed that staygreen can be induced when pods/seeds are damaged, it is unknown whether virus infection or insect infestation causes staygreen.To determine whether viral infection causes staygreen, a survey of soybean staygreen incidence in the Huang-Huai-Hai river basin was conducted in 2016 and 2017.Diseased samples were collected and analyzed using DAS-ELISA for Soybean mosaic virus, Watermelon mosaic virus, Bean pod mottle virus, Cucumber mosaic virus, and Bean common mosaic virus.The survey showed that the severity of soybean staygreen syndrome was most prevalent in Beijing, Henan, Shaanxi, and some parts of Shandong provinces, with yield losses from 0 to nearly 100%, but only a small fraction of samples were positive for the tested viruses.A field cage experiment and an insecticide treatment field trial were conducted to determine the contribution of the bean bug, Riptortus pedestris, to staygreen incidence.The field cage experiment showed that R.pedestris treatment resulted in shorter plants, more empty pods, increased numbers of abnormal seeds, and decreased yields.The field experiment showed that there were fewer R.pedestris and less soybean staygreen incidence in fields treated with insecticide than in untreated control fields.Together, these results suggest that R.pedestris infestation rather than virus infection induces staygreen syndrome and that growers in this region can mitigate staygreen syndrome via bean bug control.
基金Financial supports were provided from the Special Research Projects for Developing Transgenic Plants,China(2013ZX08011-001)the China National Science Fund for Innovative Research Groups of Biological Control(31021003)the National 973 Program of China(2007CB109202)
文摘A 2-yr field study was conducted to assess the effects of transgenic japonica rice（KMD1 and KMD2） with a synthetic cry1 Ab gene from Bacillus thuingiensis Berliner on population dynamics and seasonal average densities of five thrips species including Stenchaetothrips biformis（Bagnall）,Frankliniella intonsa（Trybom）,F.tenuicornis（Uzel）,Haplothrips aculeatus（Fabricius）,Haplothrips tritici（Kurd） and their general predatory flower bug,Orius similis Zheng as compared to the parental control rice line using the white,blue and yellow sticky card traps.Population dynamics and seasonal average densities of these five thrips species and their general predatory flower bug were not significantly affected by rice type.Additionally,the white sticky card trap was suggested to be the most suitable for monitoring the population of these five thrips species and their general predator.These results show that our tested Bt rice lines do not interrupt the population of non-target thrips species and their general predatory flower bug in the field,and also cannot result in more occurrence of these thrips species in the rice ecosystem.
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This work is supported by the National Natural Science Foundation of China under Grant Nos. 61602403 and 61402406 and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant No. 2015BAH17F01.
文摘In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects （i.e., surprise bugs）, or break pre-existing functionalities and destroy the user experience （i.e., breakage bugs）. Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE （synthetic minority over-sampling technique） ＋ KNN （K-nearest neighbours） for surprise bug identification and RUS （random under-sampling） ＋ NB （naive Bayes） for breakage bug identification outperform the Fl-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.
基金supported by the National Basic Research 973 Program of China under Grant No.2007CB310801the National Natural Science Foundation of China under Grant Nos.60873083,60803025,60703009 and 60703018+3 种基金the Natural Science Foundation of Hubei Province under Grant No.2008ABA379the Natural Science Foundation of Hubei Province for Distinguished Young Scholars under Grant No.2008CDB351the Research Fund for the Doctoral Program of Higher Education of China under Grant Nos.20070486065 and 20090141120022the Fundamental Research Funds for the Central Universities of China under Grant No.6082005
文摘The quality of a software system is partially determined by its structure（topological structure）,so the need to quantitatively analyze the quality of the structure has become eminent.In this paper a novel metric called software quality of structure（SQoS） is presented for quantitatively measuring the structural quality of object-oriented（OO） softwares via bug propagation analysis on weighted software networks（WSNs）.First,the software systems are modeled as a WSN,weighted class dependency network（WCDN）,in which classes are nodes and the interaction between every pair of classes if any is a directed edge with a weight indicating the probability that a bug in one class will propagate to the other.Then we analyze the bug propagation process in the WCDN together with the bug proneness of each class,and based on this,a metric（SQoS） to measure the structural quality of OO softwares as a whole is developed.The approach is evaluated in two case studies on open source Java programs using different software structures（one employs design patterns and the other does not） for the same OO software.The results of the case studies validate the effectiveness of the proposed metric.The approach is fully automated by a tool written in Java.
基金This work was partially supported by the 973 Program of China （2014CB340701） and the National Natural Science Foundation of China （Grant No. 61003026）.
文摘Memory leaks are a common type of defect that is hard to detect manually. Existing memory leak detection tools suffer from lack of precise interprocedural analysis and path-sensitivity. To address this problem, we present a static interprocedural analysis algorithm, that performs fully pathsensitive analysis and captures precise function behaviors, to detect memory leak in C programs. The proposed algorithm uses path-sensitive symbolic execution to track memory actions in different program paths guarded by path conditions. A novel analysis model called memory state transition graph （MSTG） is proposed to describe the tracking process and its results. In order to do interprocedural analysis, the proposed algorithm generates a summary for each procedure from MSTG and applies the summary at the procedure＇s call sites. A prototype tool called Melton is implemented for this procedure. Melton was applied to five open source C programs and 41 leaks were found. More than 90% of these leaks were subsequently confirmed and fixed by their maintainers. For comparison with other tools, Melton was also applied to some programs in standard performance evaluation corporation （SPEC） CPU 2000 benchmark suite and detected more leaks than the state of the art approaches.
文摘The large number of new bug reports received in bug repositories of software systems makes their management a challenging task.Handling these reports manually is time consuming,and often results in delaying the resolution of important bugs.To address this issue,a recommender may be developed which automatically prioritizes the new bug reports.In this paper,we propose and evaluate a classification based approach to build such a recommender.We use the Na¨ ve Bayes and Support Vector Machine （SVM） classifiers,and present a comparison to evaluate which classifier performs better in terms of accuracy.Since a bug report contains both categorical and text features,another evaluation we perform is to determine the combination of features that better determines the priority of a bug.To evaluate the bug priority recommender,we use precision and recall measures and also propose two new measures,Nearest False Negatives （NFN） and Nearest False Positives （NFP）,which provide insight into the results produced by precision and recall.Our findings are that the results of SVM are better than the Na¨ ve Bayes algorithm for text features,whereas for categorical features,Na¨ ve Bayes performance is better than SVM.The highest accuracy is achieved with SVM when categorical and text features are combined for training.
文摘Since its outbreak in December 2019 in Wuhan Province (China), the Coronavirus (COVID-19) disease quickly spread around the world in such a way that most response plans were outdated. There was an urgent need to change and adapt response strategies as the virus globally spread. Entire firms and economies were brought to a standstill in order to reduce the virus’ capacity to spread and to limit some of the short-term impacts in order to save time and find out solutions to come back to a more or less normal way of life. Thus, most of the countries that closed their air, sea and land borders had to reopen them progressively, with travel restrictions submitted to rigid controls. In Côte d’Ivoire, as in all other countries, air travellers leaving the territory were required to provide a certificate for a negative COVID-19 test, valid for 24 to 72 hours depending on the country of destination. However, the national system implemented could not provide a result before 48 hours. The objective of this work was to develop an alternative strategy to the system for air travellers who were in a hurry and those who had a computer bug in obtaining their result. A total of 38,444 air travellers benefited from this strategy implemented by the Institut Pasteur de Côte d’Ivoire during these two years.
基金supported by the National Key R&D Program of China (2018YFB1702700)。
文摘The existing software bug localization models treat the source file as natural language, which leads to the loss of syntactical and structure information of the source file. A bug localization model based on syntactical and semantic information of source code is proposed. Firstly, abstract syntax tree(AST) is divided based on node category to obtain statement sequence. The statement tree is encoded into vectors to capture lexical and syntactical knowledge at the statement level.Secondly, the source code is transformed into vector representation by the sequence naturalness of the statement. Therefore,the problem of gradient vanishing and explosion caused by a large AST size is obviated when using AST to the represent source code. Finally, the correlation between bug reports and source files are comprehensively analyzed from three aspects of syntax, semantics and text to locate the buggy code. Experiments show that compared with other standard models, the proposed model improves the performance of bug localization, and it has good advantages in mean reciprocal rank(MRR), mean average precision(MAP) and Top N Rank.
