Temporal properties are important in a wide variety of domains for different purposes. For example, they can be used to avoid architectural drift in software engineering orto support the regulatory compliance of business processes. In this work, we study the understandability of three majortemporal property representations: (1) LinearTemporal Logic (LTL) is a formal and well-established logic that offers temporal operators to describe temporal properties; (2) Property Specification Patterns (PSP) are a collection of recurring temporal properties that abstract underlying formal and technical representations; (3) Event Processing Language (EPL) can be used for runtime monitoring of event streams using Complex Event Processing. We conducted two controlled experiments with 216 participants in total to study the understandability of those approaches using a completely randomized design with one alternative per experimental unit. We hypothesized that PSP, as a highly abstracting pattern language, is easier to understand than LTL and EPL, and that EPL, due to separation of concerns (as one or more queries can be used to explicitly define the truth value change that an observed event pattern causes), is easier to understand than LTL. We found evidence supporting our hypotheses which was statistically significant and reproducible.

Mature verification and monitoring approaches, such as complex event processing and model checking, can be applied for checking compliance specifications at design time and runtime. Little is known about the understandability of the different formal and technical languages associated with these approaches. This uncertainty regarding understandability might be a major obstacle for the broad practical adoption of those techniques. This article reports a controlled experiment with 215 participants on the understandability of modeling compliance specifications in representative modeling languages, namely linear temporal logic (LTL), the complex event processing-based event processing language (EPL) and property specification patterns (PSP). The formalizations in PSP were overall more correct. That is, the pattern-based approach provides a higher level of understandability than EPL and LTL. More advanced users, however, seemingly are able to cope equally well with PSP and EPL in modeling compliance specifications.

This article reports a controlled experiment with 116 participants on the understandability of representative graphical and textual pattern-based behavioral constraint representations from the viewpoint of novice software designers. Particularly, graphical and textual behavioral constraint patterns present in the declarative business process language Declare and textual behavioral constraints based on Property Specification Patterns are the subjects of this study. In addition to measuring the understandability construct, this study assesses subjective aspects such as perceived difficulties regarding learning and application of the tested approaches. An interesting finding of this study is the overall low achieved correctness in the experimental tasks, which seems to indicate that pattern-based behavioral constraint representations are hard to understand for novice software designers in the absence of additional supportive measures. The results of the descriptive statistics regarding achieved correctness are slightly in favor of the textual representations, but the inference statistics do not indicate any significant differences in terms of understandability between graphical and textual behavioral constraint representations.

Software architecture compliance is concerned with the alignment of implementation with its desired architecture and detecting potential inconsistencies. The work presented in this paper is specifically concerned with behavioral architecture compliance. That is, the focus is on semantic alignment of implementation and architecture. In particular, this paper evaluates three representative approaches for describing semantic constraints in terms of their understandability, namely natural language descriptions as used in many architecture documentations today, a structured language based on specification patterns that abstract underlying temporal logic formulas, and a structured cause-effect language that is based on Complex Event Processing. We conducted a controlled experiment with 190 participants using a simple randomized design with one alternative per experimental unit. Overall all approaches support a high level of correct understanding, and the statistical inference suggests that all tested approaches are equally well suited for describing semantic constraints for behavioral architecture compliance in terms of understandability. In consequence this indicates that it is possible to benefit from the tested structured languages with underlying formal representations for automated verification without having to suffer from decreased understandability. Vice versa, the results suggest that the use of natural language can be a suitable way to document architecture semantics when reliable automated support for formal verification is of minor importance.

When watching video in a mobile environment, many distractions can draw the attention of the user away from the terminal, e.g., a smartphone. The power consumed by the phone during video playback is wasted in case the user is not watching. In this paper, we present an interactive video player allowing to temporarily interrupt the playback when the user is looking away, and resume it when the user starts looking at the phone again. In order to detect user inattention, we exploit the front-facing camera embedded in most current smartphones as image source for face detections. We analyze the power consumption of the phone when running our interactive video player in controlled and realistic scenarios, and study the energy savings induced by the attention-aware playback interruption. Additionally, we study the influence of video bitrate and content on power consumption. Finally, we propose a cloud-based approach in which the image captured by the front camera is sent to a server, which performs the face detection. We show that the power savings are significant using this approach, and can compensate the consumption added by the increased wireless traffic.