1,721,031 research outputs found
Evaluating count models for predicting post-release faults in object-oriented software
No full textThis thesis empirically compares statistical prediction models using fault count data and fault binary data. The types of statistical models that are studied in detail are Logistic Regression for binary data and Negative Binomial Regression for the count data. Different model building approaches are also evaluated: manual variable selection, stepwise variable selection, and hybrid selection (classification and regression trees combined with stepwise selection). The data set comes from a commercial Java application development project. In this project special attention was paid to data collection to ensure data accuracy. The comparison criteria we used were a consistency coefficient and the estimated cost savings from using the prediction model. The results indicate that while different model building approaches result in different object-oriented metrics being selected, there is no marked difference in the quality of the models that are produced. These results suggest that there is no compelling reason to collect highly accurate fault count data when building object-oriented models, and that fault binary data (which are much easier to collect) will do just as well. (Abstract shortened by UMI.
Identification and inclusion of gender factors in retrospective cohort studies: the GOING-FWD framework
Full text link©. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/
This document is the Published Manuscript version of a Published Work that appeared in final form in [BMJ Global Health]. To access the final edited and published work see[10.1136/bmjgh-2021-005413
Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data
No full textOBJECTIVE: To examine sex and gender roles in COVID-19 test positivity and hospitalisation in sex-stratified predictive models using machine learning. DESIGN: Cross-sectional study. SETTING: UK Biobank prospective cohort. PARTICIPANTS: Participants tested between 16 March 2020 and 18 May 2020 were analysed. MAIN OUTCOME MEASURES: The endpoints of the study were COVID-19 test positivity and hospitalisation. Forty-two individuals’ demographics, psychosocial factors and comorbidities were used as likely determinants of outcomes. Gradient boosting machine was used for building prediction models. RESULTS: Of 4510 individuals tested (51.2% female, mean age=68.5±8.9 years), 29.4% tested positive. Males were more likely to be positive than females (31.6% vs 27.3%, p=0.001). In females, living in more deprived areas, lower income, increased low-density lipoprotein (LDL) to high-density lipoprotein (HDL) ratio, working night shifts and living with a greater number of family members were associated with a higher likelihood of COVID-19 positive test. While in males, greater body mass index and LDL to HDL ratio were the factors associated with a positive test. Older age and adverse cardiometabolic characteristics were the most prominent variables associated with hospitalisation of test-positive patients in both overall and sex-stratified models. CONCLUSION: High-risk jobs, crowded living arrangements and living in deprived areas were associated with increased COVID-19 infection in females, while high-risk cardiometabolic characteristics were more influential in males. Gender-related factors have a greater impact on females; hence, they should be considered in identifying priority groups for COVID-19 infection vaccination campaigns
Sex and gender aspects in diabetes mellitus: Focus on access to health care and cardiovascular outcomes
No full textAimsThe aim of this study was to elucidate whether sex and gender factors influence access to health care and/or are associated with cardiovascular (CV) outcomes of individuals with diabetes mellitus (DM) across different countries. MethodsUsing data from the Canadian Community Health Survey (8.4% of respondent reporting DM) and the European Health Interview Survey (7.3% of respondents reporting DM), were analyzed. Self-reported sex and a composite measure of socio-cultural gender was constructed (range: 0-1; higher score represent participants who reported more characteristics traditionally ascribed to women). For the purposes of analyses the Gender Inequality Index (GII) was used as a country level measure of institutionalized gender. ResultsCanadian females with DM were more likely to undergo HbA1c monitoring compared to males (OR = 1.26, 95% CI: 1.01-1.58), while conversely in the European cohort females with DM were less likely to have their blood sugar measured compared to males (OR = 0.88, 95% CI: 0.79-0.99). A higher gender score in both cohorts was associated with less frequent diabetes monitoring. Additionally, independent of sex, higher gender scores were associated with higher prevalence of self-reported heart disease, stroke, and hospitalization in all countries albeit European countries with medium-high GII, conferred a higher risk of all outcomes and hospitalization rates than low GII countries. ConclusionRegardless of sex, individuals with DM who reported characteristics typically ascribed to women and those living in countries with greater gender inequity for women exhibited poorer diabetes care and greater risk of CV outcomes and hospitalizations
A Methodology for Validating Software Product Metrics
No full textA large number of software product metrics 1 have been proposed in software engineering. Product metrics quantitatively characterize some aspect of the structure of a software product, such as a requirements specification, a design, or source code. They are also commonly collectively known as complexity metrics. While many of these metrics are based on good ideas about what is important to measure in software to capture its complexity, it is still necessary to systematically validate them. Recent software engineering literature has reflected a concern for the quality of methods to validate software product metrics (e.g., see [38][80][106]). This concern is driven, at least partially, by a recognition that: (i) common practices for the validation of software engineering metrics are not acceptable on scientific grounds, and (ii) valid measures are essential for effective software project management and sound empirical research. For example, in a recent paper [80], the authors write: "Unless the software measurement community can agree on a valid, consistent, and comprehensive theory of measurement validation, we have no scientific basis for the discipline of software measurement, a situation potentially disasterous for both practice and research." Therefore, to have confidence in the utility of the many metrics that are proposed from research labs, it is crucial that they are validated.Un grand nombre de syst\ue8mes de mesure de logiciels a \ue9t\ue9 propos\ue9 en g\ue9nie logiciel. Ces syst\ue8mes caract\ue9risent quantitativement certains aspects de la structure d'un logiciel, tels que la sp\ue9cification des exigences, la construction ou le code source. Ils sont appel\ue9s collectivement syst\ue8mes de mesure de complexit\ue9. Bien que plusieurs d'entre eux soient bas\ue9s sur une bonne perception de ce qu'il est important de mesurer dans un logiciel pour saisir la complexit\ue9 de celui-ci, il est toujours n\ue9cessaire de les valider syst\ue9matiquement. La litt\ue9rature r\ue9cente du g\ue9nie logiciel a r\ue9v\ue9l\ue9 une pr\ue9occupation pour la qualit\ue9 des m\ue9thodes de validation des syst\ue8mes de mesure de logiciels (voir, p. ex. [38], [80] et [106]). Cette pr\ue9occupation est suscit\ue9e, en partie du moins, par le fait que i) les pratiques courantes de validation des syst\ue8mes de mesure utilis\ue9s en g\ue9nie logiciel ne sont pas acceptables au point de vue scientifique et ii) que des m\ue9thodes de mesure valides sont essentielles pour obtenir une gestion efficace des projets logiciels et faire des recherches empiriques sur une base solide. Par exemple, dans un r\ue9cent m\ue9moire [80], les auteurs affirment : "\ue0 moins que la communaut\ue9 des sp\ue9cialistes en mesure de logiciels n'en vienne \ue0 une entente sur une th\ue9orie valide, coh\ue9rente et exhaustive de la validation des mesures, la mesure des logiciels restera sans assise scientifique, une situation potentiellement d\ue9sastreuse pour les utilisateurs comme pour les chercheurs." Par cons\ue9quent, afin d'inspirer confiance dans leur utilit\ue9, il devient crucial de valider les nombreux syst\ue8mes de mesure propos\ue9s par les laboratoires de recherche.NRC publication: Ye
Heuristics for de-identifying health data
No full textBefore releasing personal health information for secondary uses, such as research or public health monitoring, organizations must de-identify the data they've collected. Several common heuristics are useful for this purpose, but they also have limitations. // NOTE // This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder
Spice the theory and practice of software process improvement and capability determination
No full textDescription of the SWEBOK Knowledge Area Software Engineering Process (Version 0.9)
No full textThe software engineering process Knowledge Area has witnessed dramatic growth over the last decade. This was partly due to a recognition by major acquirers of systems where software is a major component that process issues can have an important impact on the ability of their suppliers to deliver. Therefore, they encouraged a focus on the software engineering process as a way to remedy this. Furthermore, the academic community has recently pursued an active research agenda in developing new tools and techniques to support software engineering processes, and also empirically studying these processes and their improvement. It should also be recognized that many software engineering process issues are closely related to other disciplines, namely those in the management sciences, albeit they have used a different terminology. The industrial adoption of software engineering process technology has also been increasing, as demonstrated by a number of published success stories. Therefore, there is in fact an extensive body of knowledge on the software engineering process.Le processus de g\ue9nie logiciel Knowledge Area a connu une croissance importante au cours de la derni\ue8re d\ue9cennie. Cette croissante d\ue9coule en partie de la reconnaissance, par des acqu\ue9rants de syst\ue8mes d'importance dans lesquels les logiciels constituent une composante pr\ue9pond\ue9rante, que les questions li\ue9es au processus peuvent avoir une forte incidence sur la capacit\ue9 de leurs fournisseurs \ue0 livrer la marchandise voulue. Par cons\ue9quent, ceux-ci favorisaient l'utilisation accrue du processus de g\ue9nie logiciel comme moyen de redresser la situation. De plus, les universitaires ont r\ue9cemment men\ue9 un programme de recherche actif pour l'\ue9laboration de nouveaux outils et de nouvelles techniques \ue0 l'appui des processus de g\ue9nie logiciel et ont \ue9tudi\ue9 \ue9galement de fa\ue7on empirique ceux-ci ainsi que leurs am\ue9liorations possibles. Il faut aussi reconna\ueetre que bon nombre de questions relatives aux processus de g\ue9nie logiciel sont \ue9troitement li\ue9es \ue0 d'autres disciplines, nomm\ue9ment celles des sciences de la gestion, quoique la terminologie utilis\ue9e y soit diff\ue9rente. L'adoption de la technologie des processus de g\ue9nie logiciel s'est \ue9galement accrue dans l'industrie, comme le d\ue9montre un certain nombre d'exp\ue9riences \ue0 succ\ue8s publi\ue9es. Par cons\ue9quent, le bloc de connaissances relatif au processus de g\ue9nie logiciel est tr\ue8s vaste.NRC publication: Ye
- …
