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We analyze online social data to model social interactions of users in recommender systems: i) Rating prediction, and ii) detecting spammers and abnormal user rating behaviors. We propose a social trust model using matrix factorization method to estimate users taste by incorporating user-item matrix. The effect of users friends tastes is modeled based on centrality metrics and similarity algorithms...
Over-booking cloud resources is an effective way to increase the cost efficiency of a cluster, and is being studied within Microsoft for the Azure SQL Database service. A key challenge is to strike the right balance between the potentially conflicting goals of optimizing for resource allocation efficiency and positive user experience. Understanding when cloud database customers use their database...
Over time, a software system's code and its underlying design tend to decay steadily and, in turn, to complicate the system's maintenance. In order to address that phenomenon, many researchers tried to help engineers predict parts of a system that are most likely to create problems while or even before they are modifying the system. Problems that creep into a system may manifest themselves as bugs,...
Terms in source code have become extremely important in Software Engineering research. These ``important'' terms are typically used as input to research tools. Therefore, the quality of the output of these tools will depend on the quality of the term extraction technique. Currently, there is no definitive best technique for predicting the importance of terms during program comprehension. In my work,...
Recently, Technical Debt (TD) has gained popularity in the Software Engineering community to describe design decisions that allow software development teams to achieve short term benefits such as expedited release of code. Technical debt accrued should be managed to avoid the disastrous consequences of these temporary workarounds. Management of technical debt involve documenting the debt item in the...
A major challenge in Cloud computing is resource provisioning for computational tasks. Not surprisingly, previous work has established a number of solutions to provide Cloud resources in an efficient manner. However, in order to realize a holistic resource provisioning model, a prediction of the future resource consumption of upcoming computational tasks is necessary. Nevertheless, the topic of prediction...
We propose a novel method based on user's double identities in the context of social network which people can consume information as well as generate content. We acquire relationship, which we call trust, between users from users' activities that performed on the related items (i.e. resource) that authors have published. Then we improve users' ratings on items with their relationship with authors...
Video services are flourishing, and the same content might be hosted by different websites. A user typically does not care about which provider provides the service to him/her but rather the quality of the service. However, the network condition is not stable and it is hard to obtain the service performance in real time during the online service. Therefore, it is very important to understand the potential...
Solar photovoltaics (PV), one of the most promising and rapidly developing renewable energy technologies, has evolved towards becoming a main renewable electricity source. It is termed variable energy resources since solar irradiance is intermittent in nature. This variability is a critical factor when predicting the available energy of solar sources. Capital and operational costs associated with...
Predicting change-prone object-oriented software using source code metrics is an area that has attracted several researchers attention. However, predicting change-prone web services in terms of changes in the WSDL (Web Service Description Language) Interface using source code metrics implementing the services is a relatively unexplored area. We conduct a case-study on change proneness prediction on...
Bug prediction has been a hot research topic for the past two decades, during which different machine learning models based on a variety of software metrics have been proposed. Feature selection is a technique that removes noisy and redundant features to improve the accuracy and generalizability of a prediction model. Although feature selection is important, it adds yet another step to the process...
Bug prediction is a technique that strives to identify where defects will appear in a software system. Bug prediction employs machine learning to predict defects in software entities based on software metrics. These machine learning models usually have adjustable parameters, called hyperparameters, that need to be tuned for the prediction problem at hand. However, most studies in the literature keep...
Mining usage data from a large number of Android users can assist various software engineering tasks. In collaboration with Wandoujia, a leading Android app marketplace in China, we have conducted a large empirical analysis based on mining app usage behaviors collected from millions of Android users. Our empirical findings can provide implications, challenges, and opportunities to app-centric software...
Software project artifacts such as source code, requirements, and change logs represent a gold-mine of actionable information. As a result, software analytic solutions have been developed to mine repositories and answer questions such as "who is the expert?,'' "which classes are fault prone?,'' or even "who are the domain experts for these fault-prone classes?'' Analytics often require...
Just-In-Time (JIT) defect prediction models aim to predict the commits that will introduce defects in the future. Traditionally, JIT defect prediction models are trained using metrics that are primarily derived from aspects of the code change itself (e.g., the size of the change, the author’s prior experience). In addition to the code that is submitted during a commit, authors write commit messages,...
Prediction of maintainability parameter for Object-Oriented Software using source code metrics is an area that hasattracted the attention of several researchers in academia andindustry. However, maintainability prediction of Service-Orientedsoftware is a relatively unexplored area. In this work, we conductan empirical analysis on maintainability prediction of eBay webservices using several source...
Transformation of occurred rainfall into runoff generated within a catchment is a complex natural phenomenon that passes through various inter-related processes and influenced by many topographic, geographic, geologic and sociologic factors. To develop a model that can reliably imbibe the complex Rainfall-Runoff interaction, two different approaches namely, conventional regression and Artificial Neural...
Because of the volatility of memory, nodes in in-memory storage system crashing down would lead to data lost. One solution to this problem is backing data up. However, if we backup data to a node which is about to fail down, the data should be recopied again. That would lead to a large amount of backup data, and in turn reduce the system reliability. We first establish a correlated failure model with...
This paper presents analysis of existing empirical studies of software metric-based refactorings opportunities identification (ROI) for object-oriented (OO) software systems. We carried out a comprehensive analysis on sixteen (16) primary studies to identify the state-of-the-practice in ROI, focusing on their operations, refactoring activities, programming languages and the impact on software quality...
This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between...
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