EDUCATING THE PUBLIC THROUGH NEWS MEDIA: CASE STUDIES OF NEWS LITERACY IN THE NEW MEDIA LANDSCAPE

Two decades of unprecedented changes in the media landscape have increased the complexity of informing the public through news media. With significant changes to the way the news industry does business and the way news consumers access this information, a new set of skills is being proposed as essential for today’s news consumer. News literacy is the use of critical thinking skills to assess the reliability and source of the information that people consume on a daily basis, as well as fostering self-awareness of personal news consumption habits and how it can create audience bias. The purpose of this study was to examine how adults experience the news in their everyday lives and to describe the nature of the news literacy skills people employ in their daily news consumption. This study purposefully selected four adults who have completed high school, and who regularly consume news information across a number of platforms, both traditional and digital. Two of the participants, one man and one woman, were over 50 years old. One other male participant was in his 30’s and the final participant, a young woman, was in her 20’s. They all utilized both traditional and digital media on a regular basis and all had differing skill levels when using social media for information. Their news experiences were documented by in-depth interviews and the completion of seven daily news logs. In their daily logs the participants differentiated news information from other information available on-line but the interviews revealed a contradiction between their intentions and their news consumption practices. All four participants had trouble distinguishing between news and opinion pieces in the news information realm. In addition all but one seemed unaware of their personal bias and any possible effect it was having on their news consumption. Further research should explore the benefits of an adult-centered news literacy curriculum on news consumers similar to the participants, and should examine the development of audience bias and its relationship to the daily exposure people have to the torrent of information that is available to them on a daily basis.

IMPROVING THE PERFORMANCE OF DATABASE-CENTRIC APPLICATIONS THROUGH PROGRAM ANALYSIS

Modern software applications are becoming more dependent on database management systems (DBMSs). DBMSs are usually used as black boxes by software developers. For example, Object-Relational Mapping (ORM) is one of the most popular database abstraction approaches that developers use nowadays. Using ORM, objects in Object-Oriented languages are mapped to records in the database, and object manipulations are automatically translated to SQL queries. As a result of such conceptual abstraction, developers do not need deep knowledge of databases; however, all too often this abstraction leads to inefficient and incorrect database access code. Thus, this thesis proposes a series of approaches to improve the performance of database-centric software applications that are implemented using ORM. Our approaches focus on troubleshooting and detecting inefficient (i.e., performance problems) database accesses in the source code, and we rank the detected problems based on their severity. We first conduct an empirical study on the maintenance of ORM code in both open source and industrial applications. We find that ORM performance-related configurations are rarely tuned in practice, and there is a need for tools that can help improve/tune the performance of ORM-based applications. Thus, we propose approaches along two dimensions to help developers improve the performance of ORM-based applications: 1) helping developers write more performant ORM code; and 2) helping developers configure ORM configurations. To provide tooling support to developers, we first propose static analysis approaches to detect performance anti-patterns in the source code. We automatically rank the detected anti-pattern instances according to their performance impacts. Our study finds that by resolving the detected anti-patterns, the application performance can be improved by 34% on average. We then discuss our experience and lessons learned when integrating our anti-pattern detection tool into industrial practice. We hope our experience can help improve the industrial adoption of future research tools. However, as static analysis approaches are prone to false positives and lack runtime information, we also propose dynamic analysis approaches to further help developers improve the performance of their database access code. We propose automated approaches to detect redundant data access anti-patterns in the database access code, and our study finds that resolving such redundant data access anti-patterns can improve application performance by an average of 17%. Finally, we propose an automated approach to tune performance-related ORM configurations using both static and dynamic analysis. Our study shows that our approach can help improve application throughput by 27--138%. Through our case studies on real-world applications, we show that all of our proposed approaches can provide valuable support to developers and help improve application performance significantly.