The aim of this study was to examine the effects that reading on screens (using digital devices with different screen sizes) and on paper have on reading time and comprehension. The study group was comprised of126 secondary students from a private school. To ensure balance, the participants were assigned into test groups, formed according to the size of the reading device, based on class levels, gender, and average school grade. In the data collection process, a narrative text was transferred to both print and electronic media. Access to the text was provided on paper and three digital devices. There were no time limitations. After reading, participants were given comprehension tests that consisted of twenty multiple-choice questions. Female participants’ on-screen comprehension scores were found to be relatively higher compared to their paper comprehension scores. They also acquired better scores as the screen size increased. Conversely, male participants had higher comprehension scores on tablets and smart phones. Participants who read the text on smart phones and tablets completed the tasks in less time compared to other groups. However, the effects of reading modality on comprehension and reading time were not found to be statistically significant in either the male or female groups.

The software architectures of business, mission, or safety critical systems must be carefully designed to balance an exacting set of quality concerns describing characteristics such as security, reliability, and performance. Unfortunately, software architectures tend to degrade over time as maintainers modify the system without understanding the underlying architectural decisions. Although this problem can be mitigated by manually tracing architectural decisions into the code, the cost and effort required to do this can be prohibitively expensive. In this paper we therefore present a novel approach for automating the construction of traceability links for architectural tactics. Our approach utilizes machine learning methods and lightweight structural analysis to detect tactic-related classes. The detected tactic-related classes are then mapped to a Tactic Traceability Information Model. We train our trace algorithm using code extracted from fifteen performance-centric and safety-critical open source software systems and then evaluate it against the Apache Hadoop framework. Our results show that automatically generated traceability links can support software maintenance activities while helping to preserve architectural qualities.