Proceedings of the 12th International Symposium on Open Collaboration最新文献

{"title":"Predicting the quality of user contributions via LSTMs","authors":"Rakshit Agrawal, L. D. Alfaro","doi":"10.1145/2957792.2957811","DOIUrl":"https://doi.org/10.1145/2957792.2957811","url":null,"abstract":"In many collaborative systems it is useful to automatically estimate the quality of new contributions; the estimates can be used for instance to flag contributions for review. To predict the quality of a contribution by a user, it is useful to take into account both the characteristics of the revision itself, and the past history of contributions by that user. In several approaches, the user's history is first summarized into a number of features, such as number of contributions, user reputation, time from previous revision, and so forth. These features are then passed along with features of the current revision to a machine-learning classifier, which outputs a prediction for the user contribution. The summarization step is used because the usual machine learning models, such as neural nets, SVMs, etc. rely on a fixed number of input features. We show in this paper that this manual selection of summarization features can be avoided by adopting machine-learning approaches that are able to cope with temporal sequences of input. In particular, we show that Long-Short Term Memory (LSTM) neural nets are able to process directly the variable-length history of a user's activity in the system, and produce an output that is highly predictive of the quality of the next contribution by the user. Our approach does not eliminate the process of feature selection, which is present in all machine learning. Rather, it eliminates the need for deciding which features from a user's past are most useful for predicting the future: we can simply pass to the machine-learning apparatus all the past, and let it come up with an estimate for the quality of the next contribution. We present models combining LSTM and NN for predicting revision quality and show that the prediction accuracy attained is far superior to the one obtained using the NN alone. More interestingly, we also show that the prediction attained is superior to the one obtained using user reputation as a feature summarizing the quality of a user's past work. This can be explained by noting that the primary function of user reputation is to provide an incentive towards performing useful contributions, rather than to be a feature optimized for prediction of future contribution quality. We also show that the LSTM output changes in a natural way in response to user behavior, increasing when the user performs a sequence of good quality contributions, and decreasing when the user performs a sequence of low-quality work. The LSTM output for a user could thus be usefully shown to other users, alongside the user's reputation and other information.","PeriodicalId":297748,"journal":{"name":"Proceedings of the 12th International Symposium on Open Collaboration","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123636886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing OSM Area-Boundary Data to DBpedia","authors":"Doris Silbernagl, Nikolaus Krismer, Günther Specht","doi":"10.1145/2957792.2957806","DOIUrl":"https://doi.org/10.1145/2957792.2957806","url":null,"abstract":"OpenStreetMap (OSM) is a well known and widely used data source for geographic data. This kind of data can also be found in Wikipedia in the form of geographic locations, such as cities or countries. Next to the geographic coordinates, also statistical data about the area of these elements can be present. Since it is possible to extract these data from OpenStreetMap as well, it is sensible to examine the quality of the OSM information about those specific boundary elements and compare them to an also crowd-sourced source like Wikipedia. Hence, in this paper OSM data of different countries are used to calculate the area of valid boundary (multi-) polygons and are then compared to the respective DBpedia (a large-scale knowledge base extract from Wikipedia) entries.","PeriodicalId":297748,"journal":{"name":"Proceedings of the 12th International Symposium on Open Collaboration","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133167709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Observing Custom Software Modifications: A Quantitative Approach of Tracking the Evolution of Patch Stacks","authors":"Ralf Ramsauer, D. Lohmann, W. Mauerer","doi":"10.1145/2957792.2957810","DOIUrl":"https://doi.org/10.1145/2957792.2957810","url":null,"abstract":"Modifications to open-source software (OSS) are often provided in the form of \"patch stacks\" -- sets of changes (patches) that modify a given body of source code. Maintaining patch stacks over extended periods of time is problematic when the underlying base project changes frequently. This necessitates a continuous and engineering-intensive adaptation of the stack. Nonetheless, long-term maintenance is an important problem for changes that are not integrated into projects, for instance when they are controversial or only of value to a limited group of users. We present and implement a methodology to systematically examine the temporal evolution of patch stacks, track non-functional properties like integrability and maintainability, and estimate the eventual economic and engineering effort required to successfully develop and maintain patch stacks. Our results provide a basis for quantitative research on patch stacks, including statistical analyses and other methods that lead to actionable advice on the construction and long-term maintenance of custom extensions to OSS.","PeriodicalId":297748,"journal":{"name":"Proceedings of the 12th International Symposium on Open Collaboration","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124617244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}