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Software developers spend a significant portion of their resources handling user-submitted bug reports. For software that is widely deployed, the number of bug reports typically outstrips the resources available to triage them. As a result, some reports may be dealt with too slowly or not at all.We present a descriptive model of bug report quality based on a statistical analysis of surface features of over 27,000 publicly available bug reports for the Mozilla Firefox project. The model predicts whether a bug report is triaged within a given amount of time. Our analysis of this model has implications for bug reporting systems and suggests features that should be emphasized when composing bug reports. We evaluate our model empirically based on its hypothetical performance as an automatic filter of incoming bug reports. Our results show that our model performs significantly better than chance in terms of precision and recall. In addition, we show that our model can reduce the overall cost of software maintenance in a setting where the average cost of addressing a bug report is more than 2% of the cost of ignoring an important bug report.

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Residential experience and residential environment choice over the life-course Feijten, P.; Hooimeijer, P.; Mulder, C.H. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. AbstractThe study reported in this article answers the question: how does experience with a certain type of residential environment contribute to the explanation of residential environment choice? The issues under investigation are whether residential experience with cities, suburbs and rural areas increases the probability of return migration and whether residential experience increases the probability of moving to other places with the same type of residential environment. The probability of moving to a city, suburb or rural area is investigated by applying multinomial logistic regression on a retrospective dataset of life-courses of more than 3000 Netherlands respondents. The results indicate that city experience and suburb experience only increase the probability of return migration, whereas rural experience also increases the probability of moving to another rural area. IntroductionThe residential environment-often categorised as urban, suburban and rural-is an important feature in residential choice (Michelson, 1977;Courgeau, 1989; Deurloo 0042-0980 Print/1360-063X Online

Many studies have examined the effects of neighbourhoods on educational outcomes. The results of these studies are often conflicting, even if the same independent variables (such as poverty, educational climate, social disorganisation, or ethnic composition) are used. A systematic meta-analysis may help to resolve this lack of external validity. We identified 5516 articles from which we selected 88 that met all of the inclusion criteria. Using meta-regression, we found that the relation between neighbourhoods and individual educational outcomes is a function of neighbourhood poverty, the neighbourhood’s educational climate, the proportion of ethnic/migrant groups, and social disorganisation in the neighbourhood. The variance in the findings from different studies can partly be explained by the sampling design and the type of model used in each study. More important is the use of control variables (school, family SES, and parenting variables) in explaining the variation in the strength of neighbourhood effects.