Data Mining - Concepts and Techniques.

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

C4.5: Programs for Machine Learning (書評)

4. Modelling Survival Data in Medical Research

/pdf/how-to-conduct-surveys-a-step-by-step-guide-4za2dab8jy.pdf

How to Conduct Surveys: A Step-by-Step Guide

Multi-tenancy is a relatively new software architecture principle in the realm of the Software as a Service (SaaS) business model. It allows to make full use of the economy of scale, as multiple customers - "tenants" - share the same application and database instance. All the while, the tenants enjoy a highly configurable application, making it appear that the application is deployed on a dedicated server. The major benefits of multi-tenancy are increased utilization of hardware resources and improved ease of maintenance, in particular on the deployment side. These benefits should result in lower overall application costs, making the technology attractive for service providers targeting small and medium enterprises (SME). However, as this paper advocates, a wrong architectural choice might entail that multi-tenancy becomes a maintenance nightmare.

Multi-tenant SaaS applications: maintenance dream or nightmare?

Multi-tenancy is a relatively new software architecture principle in the realm of the Software as a Service (SaaS) business model. It allows to make full use of the economy of scale, as multiple customers - “tenants” - share the same application and database instance. All the while, the tenants enjoy a highly configurable application, making it appear that the application is deployed on a dedicated server. The major benefits of multi-tenancy are increased utilization of hardware resources and improved ease of maintenance, resulting in lower overall application costs, making the technology attractive for service providers targeting small and medium enterprises (SME). Therefore, migrating existing single-tenant to multi-tenant applications can be interesting for SaaS software companies. In this paper we report on our experiences with reengineering an existing industrial, single-tenant software system into a multitenant one using a lightweight reengineering approach.

/pdf/enabling-multi-tenancy-an-industrial-experience-report-2m80eusb5h.pdf

Enabling multi-tenancy: An industrial experience report

Technical question and answer Q&A platforms, such as Stack Overflow, provide a platform for users to ask and answer questions about a wide variety of programming topics. These platforms accumulate a large amount of knowledge, including hundreds of thousands lines of source code. Developers can benefit from the source code that is attached to the questions and answers on Q&A platforms by copying or learning from (parts of) it. By understanding how developers utilize source code from Q&A platforms, we can provide insights for researchers which can be used to improve next-generation Q&A platforms to help developers reuse source code fast and easily. In this paper, we first conduct an exploratory study on 289 files from 182 open-source projects, which contain source code that has an explicit reference to a Stack Overflow post. Our goal is to understand how developers utilize code from Q&A platforms and to reveal barriers that may make code reuse more difficult. In 31.5% of the studied files, developers needed to modify source code from Stack Overflow to make it work in their own projects. The degree of required modification varied from simply renaming variables to rewriting the whole algorithm. Developers sometimes chose to implement an algorithm from scratch based on the descriptions from Stack Overflow answers, even if there was an implementation readily available in the post. In 35.5% of the studied files, developers used Stack Overflow posts as an information source for later reference. To further understand the barriers of reusing code and to obtain suggestions for improving the code reuse process on Q&A platforms, we conducted a survey with 453 open-source developers who are also on Stack Overflow. We found that the top 3 barriers that make it difficult for developers to reuse code from Stack Overflow are: (1) too much code modification required to fit in their projects, (2) incomprehensive code, and (3) low code quality. We summarized and analyzed all survey responses and we identified that developers suggest improvements for future Q&A platforms along the following dimensions: code quality, information enhancement & management, data organization, license, and the human factor. For instance, developers suggest to improve the code quality by adding an integrated validator that can test source code online, and an outdated code detection mechanism. Our findings can be used as a roadmap for researchers and developers to improve code reuse.

How do developers utilize source code from stack overflow

Defect prediction is an important task for preserving software quality. Most prior work on defect prediction uses software features, such as the number of lines of code, to predict whether a file or commit will be defective in the future. There are several reasons to keep the number of features that are used in a defect prediction model small. For example, using a small number of features avoids the problem of multicollinearity and the so-called ‘curse of dimensionality’. Feature selection and reduction techniques can help to reduce the number of features in a model. Feature selection techniques reduce the number of features in a model by selecting the most important ones, while feature reduction techniques reduce the number of features by creating new, combined features from the original features. Several recent studies have investigated the impact of feature selection techniques on defect prediction. However, there do not exist large-scale studies in which the impact of multiple feature reduction techniques on defect prediction is investigated. In this paper, we study the impact of eight feature reduction techniques on the performance and the variance in performance of five supervised learning and five unsupervised defect prediction models. In addition, we compare the impact of the studied feature reduction techniques with the impact of the two best-performing feature selection techniques (according to prior work). The following findings are the highlights of our study: (1) The studied correlation and consistency-based feature selection techniques result in the best-performing supervised defect prediction models, while feature reduction techniques using neural network-based techniques (restricted Boltzmann machine and autoencoder) result in the best-performing unsupervised defect prediction models. In both cases, the defect prediction models that use the selected/generated features perform better than those that use the original features (in terms of AUC and performance variance). (2) Neural network-based feature reduction techniques generate features that have a small variance across both supervised and unsupervised defect prediction models. Hence, we recommend that practitioners who do not wish to choose a best-performing defect prediction model for their data use a neural network-based feature reduction technique.

The impact of feature reduction techniques on defect prediction models

The popularity of mobile apps continues to grow over the past few years. Mobile app stores, such as the Google Play Store and Apple’s App Store provide a unique user feedback mechanism to app developers through the possibility of posting app reviews. In the Google Play Store (and soon in the Apple App Store), developers are able to respond to such user feedback. Over the past years, mobile app reviews have been studied excessively by researchers. However, much of prior work (including our own prior work) incorrectly assumes that reviews are static in nature and that users never update their reviews. In a recent study, we started analyzing the dynamic nature of the review-response mechanism. Our previous study showed that responding to a review often has a positive effect on the rating that is given by the user to an app. In this paper, we revisit our prior finding in more depth by studying 4.5 million reviews with 126,686 responses for 2,328 top free-to-download apps in the Google Play Store. One of the major findings of our paper is that the assumption that reviews are static is incorrect. In particular, we find that developers and users in some cases use this response mechanism as a rudimentary user support tool, where dialogues emerge between users and developers through updated reviews and responses. Even though the messages are often simple, we find instances of as many as ten user-developer back-and-forth messages that occur via the response mechanism. Using a mixed-effect model, we identify that the likelihood of a developer responding to a review increases as the review rating gets lower or as the review content gets longer. In addition, we identify four patterns of developers: 1) developers who primarily respond to only negative reviews, 2) developers who primarily respond to negative reviews or to reviews based on their contents, 3) developers who primarily respond to reviews which are posted shortly after the latest release of their app, and 4) developers who primarily respond to reviews which are posted long after the latest release of their app. We perform a qualitative analysis of developer responses to understand what drives developers to respond to a review. We manually analyzed a statistically representative random sample of 347 reviews with responses for the top ten apps with the highest number of developer responses. We identify seven drivers that make a developer respond to a review, of which the most important ones are to thank the users for using the app and to ask the user for more details about the reported issue. Our findings show that it can be worthwhile for app owners to respond to reviews, as responding may lead to an increase in the given rating. In addition, our findings show that studying the dialogue between user and developer can provide valuable insights that can lead to improvements in the app store and user support process.

Cor-Paul Bezemer

Papers

Multi-tenant SaaS applications: maintenance dream or nightmare?

Enabling multi-tenancy: An industrial experience report

How do developers utilize source code from stack overflow

The impact of feature reduction techniques on defect prediction models

Studying the dialogue between users and developers of free apps in the Google Play Store