Changes in spatiotemporal data may often go unnoticed due to their inherent noise and low variability (e.g., geological processes over years). Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the temporal changes in such data. We propose ContourDiff, a vector-based visualization over contour plots to visualize the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors along the contour paths, revealing differential trends that the contour lines experienced over time. We evaluated our visualization using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data.

Context: Technical Debt needs to be managed to avoid disastrous consequences, and investigating developers' habits concerning technical debt management is invaluable information in software development. Objective: This study aims to characterize how developers manage technical debt based on the code smells they induce and the refactorings they apply. Method: We mined a publicly-available Technical Debt dataset for Git commit information, code smells, coding violations, and refactoring activities for each developer of a selected project. Results: By combining this information, we profile developers to recognize prolific coders, highlight activities that discriminate among developer roles (reviewer, lead, architect), and estimate coding maturity and technical debt tolerance.

Error recovery in compilers often involves significant amounts of cognitive effort to identify the code and execution points in the compiler that are related to identifying and handling input-program errors. This is because current implementations fail to explicitly identify error-related control dependencies, and to separately characterize the actions to take when programming errors are detected. As a result, compiler writers need to navigate and understand much of the compiler source in order to replace or extend error recovery actions. In the context of AspectJ compiler (ajc), this paper encapsulates error concerns as aspects yielding improved modularity through: pointcuts that explicitly declare the loci of error detection, and advice that expose the extension points for error handling.

A characteristic of aspect-oriented programming, as embodied in Aspect J, is the use of advice and point cuts to define behavior that crosscuts the structure of the rest of the code. The events during execution at which advice may execute are called join points . A pointcut is a set of join points. An advice is an action to be taken at the join points in a particular pointcut. In this model of aspect-oriented programming, join points are dynamic in that they refer to events during the flow of execution of the program.We give a denotational semantics for a minilanguage that embodies the key features of dynamic join points, pointcuts, and advice. This is the first semantics for aspect-oriented programming that handles dynamic join points and recursive procedures. It is intended as a baseline semantics against which future correctness results may be measured.