What influences the speed of prototyping? An empirical investigation of twenty software startups

It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds, firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startups progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototypingComment: This is the author's version of the work. Copyright owner's version can be accessed at doi.org/10.1007/978-3-319-57633-6_2, XP2017, Cologne, German

Statistical Analysis for Revealing Defects in Software Projects

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementDefect detection in software is the procedure to identify parts of software that may comprise defects. Software companies always seek to improve the performance of software projects in terms of quality and efficiency. They also seek to deliver the soft-ware projects without any defects to the communities and just in time. The early revelation of defects in software projects is also tried to avoid failure of those projects, save costs, team effort, and time. Therefore, these companies need to build an intelligent model capable of detecting software defects accurately and efficiently. This study seeks to achieve two main objectives. The first goal is to build a statistical model to identify the critical defect factors that influence software projects. The second objective is to build a statistical model to reveal defects early in software pro-jects as reasonable accurately. A bibliometric map (VOSviewer) was used to find the relationships between the common terms in those domains. The results of this study are divided into three parts: In the first part The term "software engineering" is connected to "cluster," "regression," and "neural network." Moreover, the terms "random forest" and "feature selection" are connected to "neural network," "recall," and "software engineering," "cluster," "regression," and "fault prediction model" and "software defect prediction" and "defect density." In the second part We have checked and analyzed 29 manuscripts in detail, summarized their major contributions, and identified a few research gaps. In the third part Finally, software companies try to find the critical factors that affect the detection of software defects and find any of the intelligent or statistical methods that help to build a model capable of detecting those defects with high accuracy. Two statistical models (Multiple linear regression (MLR) and logistic regression (LR)) were used to find the critical factors and through them to detect software defects accurately. MLR is executed by using two methods which are critical defect factors (CDF) and premier list of software defect factors (PLSDF). The accuracy of MLR-CDF and MLR-PLSDF is 82.3 and 79.9 respectively. The standard error of MLR-CDF and MLR-PLSDF is 26% and 28% respectively. In addition, LR is executed by using two methods which are CDF and PLSDF. The accuracy of LR-CDF and LR-PLSDF is 86.4 and 83.8 respectively. The standard error of LR-CDF and LR-PLSDF is 22% and 25% respectively. Therefore, LRCDF outperforms on all the proposed models and state-of-the-art methods in terms of accuracy and standard error

Online Tensor Methods for Learning Latent Variable Models

We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles. We consider decomposition of moment tensors using stochastic gradient descent. We conduct optimization of multilinear operations in SGD and avoid directly forming the tensors, to save computational and storage costs. We present optimized algorithm in two platforms. Our GPU-based implementation exploits the parallelism of SIMD architectures to allow for maximum speed-up by a careful optimization of storage and data transfer, whereas our CPU-based implementation uses efficient sparse matrix computations and is suitable for large sparse datasets. For the community detection problem, we demonstrate accuracy and computational efficiency on Facebook, Yelp and DBLP datasets, and for the topic modeling problem, we also demonstrate good performance on the New York Times dataset. We compare our results to the state-of-the-art algorithms such as the variational method, and report a gain of accuracy and a gain of several orders of magnitude in the execution time.Comment: JMLR 201

VAS (Visual Analysis System): An information visualization engine to interpret World Wide Web structure

People increasingly encounter problems of interpreting and filtering mass quantities of information. The enormous growth of information systems on the World Wide Web has demonstrated that we need systems to filter, interpret, organize and present information in ways that allow users to use these large quantities of information. People need to be able to extract knowledge from this sometimes meaningful but sometimes useless mass of data in order to make informed decisions. Web users need to have some kind of information about the sort of page they might visit, such as, is it a rarely referenced or often-referenced page? This master\u27s thesis presents a method to address these problems using data mining and information visualization techniques