User feedback is imperative in improving software quality. In this paper, we explore the rich set of user feedback available for third party mobile applications as a way to extract new/changed requirements for next versions. A potential problem using this data is its volume and the time commitment involved in extracting new/changed requirements. Our goal is to alleviate part of the process through automatic topic extraction. We process user comments to extract the main topics mentioned as well as some sentences representative of those topics. This information can be useful for requirements engineers to revise the requirements for next releases. Our approach relies on adapting information retrieval techniques including topic modeling and evaluating them on different publicly available data sets. Results show that the automatically extracted topics match the manually extracted ones, while also significantly decreasing the manual effort.

Software engineers make decisions about the design of the software they are creating on a daily basis. These decisions may impact the application in terms of efficiency, usability, flexibility, etc. Different competing design decisions are therefore often evaluated in terms of their projected impact on quality metrics prior to implementation. Recently energy has become a concern for software systems, ranging from mobile devices to large data centers. Additionally, it has been recognized that the software executing on a computing device can have a significant impact on the device's energy consumption. This raises the obvious question of whether or not it is possible to reduce the energy consumption of a software system by the means of software design decisions.This work examines how the use of different servers impacts the energy consumption of a web application. Through a controlled empirical experiment we have discovered several important findings in this regard. The results indicate that the energy consumption of a web application can vary greatly depending on the web server used to handle its requests. Furthermore, different web servers are more or less energy efficient depending on which web application features are being executed. The paper details an analysis of the results of the experiment.

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As the use of computers has grown, so too has concern about the amount of power that they consume. Data centers, for example, are limited in scalability as they struggle with soaring energy costs as many large companies rely on fast, reliable, and round-the-clock computing services. On large-scale computing clusters, like data centers, even a small drop in power consumption can have large effects. Across computing contexts, reducing power consumed by computers has become a major focus. In this paper, we present a new approach and tool for mapping software design to power consumption and describe how such mappings can provide software designers and developers useful information about the power behavior of the software they are developing. The goal is for software engineers to use this information in designing and developing more energy efficient solutions.