We introduce Approximate Unrolling, a compiler loop optimization that reduces execution time and energy consumption, exploiting code regions that can endure some approximation and still produce acceptable results. Specifically, this work focuses on counted loops that map a function over the elements of an array. Approximate Unrolling transforms loops similarly to Loop Unrolling. However, unlike its exact counterpart, our optimization does not unroll loops by adding exact copies of the loop's body. Instead, it adds code that interpolates the results of previous iterations.

Mobile health (mHealth) apps have been adopted in healthcare areas such as the management of diabetes, the monitoring of physical activities and the treatment of HIV. The users of mHealth may be patients with handicaps such as motor impairments, difficulties to remember and psychiatric conditions. Hence, assumptions normally made on other types of apps, like the user's ability to type or remember a password, might not hold in the mHealth area. This paper evaluates how different authentication approaches impact mHealth apps usability. Second, we present new metrics to evaluate ease-of-use and third, we evaluate the usability of two common authentication approaches for mHealth apps via several key process aspects and their impact on users. Based on these results, we propose a QR-Code based authentication approach for mHealth apps, which helps overcome common impediments faced by mHealth apps users.

Microbenchmarking evaluates, in isolation, the execution time of small code segments that play a critical role in large applications. The accuracy of a microbenchmark depends on two critical tasks: wrap the code segment into a payload that faithfully recreates the execution conditions of the large application; build a scaffold that runs the payload a large number of times to get a statistical estimate of the execution time. While recent frameworks such as the Java Microbenchmark Harness (JMH) address the scaffold challenge, developers have very limited support to build a correct payload. This work focuses on the automatic generation of payloads, starting from a code segment selected in a large application. Our generative technique prevents two of the most common mistakes made in microbenchmarks: dead code elimination and constant folding. A microbenchmark is such a small program that can be “over-optimized” by the JIT and result in distorted time measures, if not designed carefully. Our technique automatically extracts the segment into a compilable payload and generates additional code to prevent the risks of “over-optimization”. The whole approach is embedded in a tool called AutoJMH, which generates payloads for JMH scaffolds. We validate the capabilities AutoJMH, showing that the tool is able to process a large percentage of segments in real programs. We also show that AutoJMH can match the quality of payloads handwritten by performance experts and outperform those written by professional Java developers without experience in microbenchmarking.