Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (leaves 82-92).

Bug-fixing, or fixing known errors in computer software, makes up a large portion of software development expenses. Once a bug is discovered, it must be assigned to an appropriate developer who has the necessary expertise to fix the bug. This bug-assignment task has traditionally been done manually. However, this manual task is time-consuming, error-prone, and tedious. Therefore, automatic bug assignment techniques have been developed to facilitate this task. Most of the existing techniques are report-based. That is, they work on bugs that are textually described in bug reports. However, only a subset of bugs that are observed as a faulty program execution are also described textually. Certain bugs, such as security vulnerability bugs, are only represented with a faulty program execution, and are not described textually. In other words, these bugs are represented by a code coverage, which indicates which lines of source code have been executed in the faulty program execution. Promptly fixing these software security vulnerability bugs is necessary in order to manage security threats. Accordingly, execution-based bug assignment techniques, which model a bug with a faulty program execution, are an important tool in fixing software security bugs. In this thesis, we compare WhoseFault, an existing execution-based bug assignment technique, to report-based techniques. Additionally, we propose DR_BEV (Developer Recommendation Based on Executed Vocabulary), a novel execution-based technique that models developer expertise based on the vocabulary of each developer's source code contributions, and we demonstrate that this technique out-performs the current state-of-the-art execution-based technique. Our observations indicate that report-based techniques perform better than execution-based techniques, but not by a wide margin. Therefore, while a report-based technique should be used if a report exists for a bug, our results should provide confidence in the scenarios in which only execution-based techniques are applicable. / Master of Science / Bug-fixing, or fixing known errors in computer software, makes up a large portion of software development expenses. Once a bug is discovered, it must be assigned to an appropriate developer who has the necessary expertise to fix the bug. This bug-assignment task has traditionally been done manually. However, this manual task is time-consuming, error-prone, and tedious. Therefore, automatic bug assignment techniques have been developed to facilitate this task. Most of the existing techniques are report-based. That is, they work on bugs that are textually described in bug reports. However, only a subset of bugs that are observed as a faulty program execution are also described textually. Certain bugs, such as security vulnerability bugs, are only represented with a faulty program execution, and are not described textually. In other words, these bugs are represented by a code coverage, which indicates which lines of source code have been executed in the faulty program execution. Promptly fixing these software security vulnerability bugs is necessary in order to manage security threats. Accordingly, execution-based bug assignment techniques, which model a bug with a faulty program execution, are an important tool in fixing software security bugs. In this thesis, we compare WhoseFault, an existing execution-based bug assignment technique, to report-based techniques. Additionally, we propose DR_BEV (Developer Recommendation Based on Executed Vocabulary), a novel execution-based technique that models developer expertise based on the vocabulary of each developer's source code contributions, and we demonstrate that this technique out-performs the current state-of-the-art execution-based technique.

Open source projects incorporate bug triagers to help with the task of bug report assignment to developers. One of the tasks of a triager is to identify whether an incoming bug report is a duplicate of a pre-existing report. In order to detect duplicate bug reports, a triager either relies on his memory and experience or on the search capabilties of the bug repository. Both these approaches can be time consuming for the triager and may also lead to the misidentication of duplicates. It has also been suggested that duplicate bug reports are not necessarily harmful, instead they can complement each other to provide additional information for developers to investigate the defect at hand. This motivates the need for automated or semi-automated techniques for duplicate bug detection. In the literature, two main approaches have been proposed to solve this problem. The first approach is to prevent duplicate reports from reaching developers by automatically filtering them while the second approach deals with providing the triager a list of top-N similar bug reports, allowing the triager to compare the incoming bug report with the ones provided in the list. Previous works have tried to enhance the quality of the suggested lists, but the approaches either suffered a poor Recall Rate or they incurred additional runtime overhead, making the deployment of a retrieval system impractical. To the extent of our knowledge, there has been little work done to do an exhaustive comparison of the performance of different Information Retrieval Models (especially using more recent techniques such as topic modeling) on this problem and understanding the effectiveness of different heuristics across various application domains. In this thesis, we compare the performance of word based models (derivatives of the Vector Space Model) such as TF-IDF, Log-Entropy with that of topic based models such as Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA) and Random Indexing (RI). We leverage heuristics that incorporate exception stack frames, surface features, summary and long description from the free-form text in the bug report. We perform experiments on subsets of bug reports from Eclipse and Firefox and achieve a recall rate of 60% and 58% respectively. We find that word based models, in particular a Log-Entropy based weighting scheme, outperform topic based ones such as LSI and LDA. Using historical bug data from Eclipse and NetBeans, we determine the optimal time frame for a desired level of duplicate bug report coverage. We realize an Online Duplicate Detection Framework that uses a sliding window of a constant time frame as a first step towards simulating incoming bug reports and recommending duplicates to the end user.

Open source projects incorporate bug triagers to help with the task of bug report assignment to developers. One of the tasks of a triager is to identify whether an incoming bug report is a duplicate of a pre-existing report. In order to detect duplicate bug reports, a triager either relies on his memory and experience or on the search capabilties of the bug repository. Both these approaches can be time consuming for the triager and may also lead to the misidentication of duplicates. It has also been suggested that duplicate bug reports are not necessarily harmful, instead they can complement each other to provide additional information for developers to investigate the defect at hand. This motivates the need for automated or semi-automated techniques for duplicate bug detection. In the literature, two main approaches have been proposed to solve this problem. The first approach is to prevent duplicate reports from reaching developers by automatically filtering them while the second approach deals with providing the triager a list of top-N similar bug reports, allowing the triager to compare the incoming bug report with the ones provided in the list. Previous works have tried to enhance the quality of the suggested lists, but the approaches either suffered a poor Recall Rate or they incurred additional runtime overhead, making the deployment of a retrieval system impractical. To the extent of our knowledge, there has been little work done to do an exhaustive comparison of the performance of different Information Retrieval Models (especially using more recent techniques such as topic modeling) on this problem and understanding the effectiveness of different heuristics across various application domains. In this thesis, we compare the performance of word based models (derivatives of the Vector Space Model) such as TF-IDF, Log-Entropy with that of topic based models such as Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA) and Random Indexing (RI). We leverage heuristics that incorporate exception stack frames, surface features, summary and long description from the free-form text in the bug report. We perform experiments on subsets of bug reports from Eclipse and Firefox and achieve a recall rate of 60% and 58% respectively. We find that word based models, in particular a Log-Entropy based weighting scheme, outperform topic based ones such as LSI and LDA. Using historical bug data from Eclipse and NetBeans, we determine the optimal time frame for a desired level of duplicate bug report coverage. We realize an Online Duplicate Detection Framework that uses a sliding window of a constant time frame as a first step towards simulating incoming bug reports and recommending duplicates to the end user.

Typescript. / Thesis (Ph. D.)--University of Hawaii, 1969. / Bibliography: leaves 82-86. / x, 86 l illus. (part col.), tables

Thesis (Ph.D.) - University of Queensland, 1997. / Includes bibliography.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1948. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 55-57).

Stink bugs (Hemiptera: Pentatomidae) are key pests of soybean, Glycine max (L.), in Mississippi. Historically, yield loss derived from direct feeding by stink bugs has been considered the greatest threat to producers. However, quality reductions resulting from seed infections caused by microorganisms including Phomopsis longicolla are also a concern. Experiments were conducted in 2010 and 2011 to determine if stink bugs are associated with the incidence of P. longicolla in Mississippi soybean production. Data from experiments suggest that stink bugs are capable of transporting P. longicolla between two points. Data further suggest stink bugs and P. longicolla have the potential to cause a yield loss of 20% when combined in soybean. Surveys of commercial fields in Mississippi determined that stink bug damaged seed was more likely to be infested with P. longicolla and other fungi compared to undamaged seed.

Made available in DSpace on 2014-06-12T15:53:57Z (GMT). No. of bitstreams: 2 arquivo1938_1.pdf: 2696606 bytes, checksum: c2ff3cbbb3029fd0f89eb8d67c0e4f08 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Manutenção e evolução de software são atividades caracterizadas pelo seu enorme custo e baixa velocidade de execução. Não obstante, elas são atividades inevitáveis para garantir a qualidade do software quase todo software bem sucedido estimula os usuários a fazer pedidos de mudanças e melhorias. Sommerville é ainda mais enfático e diz que mudanças em projetos de software são um fato. Além disso, diferentes estudos têm afirmado ao longo dos anos que as atividades de manutenção e evolução de software são as mais caras do ciclo de desenvolvimento, sendo responsável por cerca de até 90% dos custos. Todas essas peculiaridades da fase de manutenção e evolução de software leva o mundo acadêmico e industrial a investigar constantemente novas soluções para reduzir os custos dessas atividades. Neste contexto, Gerência de Configuração de Software (GCS) é um conjunto de atividades e normas para a gestão da evolução e manutenção de software; GCS define como são registradas e processadas todas as modificações, o impacto das mesmas em todo o sistema, dentre outros procedimentos. Para todas estas tarefas de GCM existem diferentes ferramentas de auxílio, tais como sistemas de controle de versão e bug trackers. No entanto, alguns problemas podem surgir devido ao uso das mesmas, como por exemplo o problema de atribuição automática de responsável por um bug report e o problema de duplicação de bug reports. Neste sentido, esta dissertação investiga o problema de duplicação de bug reports resultante da utilização de bug trackers em projetos de desenvolvimento de software. Tal problema é caracterizado pela submissão de dois ou mais bug reports que descrevem o mesmo problema referente a um software, tendo como principais conseqüências a sobrecarga de trabalho na busca e análise de bug reports, e o mal aproveitamento do tempo destinado a essa atividade

Today’s world is a network of interconnected systems that are always running to facilitate information exchange so people can carry out their daily activities. Software applications are constantly evolving to meet the increasing expectations of the growing market, thereby giving rise to the development of large complex systems. It is very likely for these complex systems to encounter bugs which is a situation that can cause errors in software. These bugs can prevent the systems from operating as intended, slowing down software development and deployment, and causing delays in deadlines. This study undertook a systematic literature review to find trends in the field of bug prioritization. Software bug prioritization can help developers determine the order of fixing bugs by assigning priority levels based on the severity analysis. This study aims to identify the most promising techniques that can change the bug prediction and resolution process. It is observed that machine learning techniques (ML) have been gaining popularity in addressing the bug prioritization issue since they can automatically assign priority levels. However, these ML techniques also have limitations addressed in this study along with a taxonomic classification of identified techniques. The review obtained 34 manuscripts based on study selection criteria. These manuscripts discovered 63 unique bug prioritization techniques, including a mix of ML, data reduction and hybrid techniques. It is evident that though these techniques perform automatic prioritization, they can sometimes be slow and lack consistency in the accuracy of results.