The spread of mobile devices in modern societies has forced the industry to create software paradigms to meet the new challenges it faces. Some of these challenges are the huge heterogeneity of devices or the quick changes of users’ context. In this scenario, context becomes a key element, enabling mobile applications to be user centric and adapt to user requirements. The Mobile Context Framework, proposed in this paper, is a contribution to solve some of these challenges. Using Web servers running on the devices, context data can be provided to web applications. Besides the framework’s architecture, a prototype is presented as proof of concept of the platform’s potential.

Distributed transaction processing has benefited greatly from optimistic concurrency control protocols thus avoiding costly fine-grained synchronization. However, the performance of these protocols degrades significantly when the workload increases, namely, by leading to a substantial amount of aborted transactions due to concurrency conflicts. Our approach stems from the observation that when the abort rate increases with the load as already executed transactions queue for longer periods of time waiting for their turn to be certified and committed. We thus propose an adaptive algorithm for judiciously scheduling transactions to minimize the time during which these are vulnerable to being aborted by concurrent transactions, thereby reducing the overall abort rate. We do so by throttling transaction execution using an adaptive mechanism based on the locally known state of globally executing transactions, that includes out-of-order execution.
Our evaluation using traces from the industry standard TPC-E workload shows that the amount of aborted transactions can be kept bounded as system load increases, while at the same time fully utilizing system resources and thus scaling transaction processing throughput.

Self-diagnosis is a fundamental capability of self-adaptive systems. In order to recover from faults, systems need to know which part is responsible for the incorrect behavior. In previous work we showed how to apply a design-time diagnosis technique at run time to identify faults at the architectural level of a system. Our contributions address three major shortcomings of our previous work: 1) we present an expressive, hierarchical language to describe system behavior that can be used to diagnose when a system is behaving different to expectation; the hierarchical language facilitates mapping low level system events to architecture level events; 2) we provide an automatic way to determine how much data to collect before an accurate diagnosis can produced; and 3) we develop a technique that allows the detection of correlated faults between components. Our results are validated experimentally by injecting several failures in a system and accurately diagnosing them using our algorithm.

Automated diagnosis of errors and/or failures detected during software testing can greatly improve the eciency of the debugging process, and thus help to make applications more reliable. In this paper, we propose an approach, dubbed MZoltar, oering dynamic analysis (namely, spectrum- based fault localization) of mobile apps that produces a diagnostic report to help identifying potential defects quickly. The approach also oers a graphical representation of the diagnostic report, making it easier to understand. Our experimental results show that the approach requires low runtime overhead (5.75% on average), while the tester needs to inspect 5 components (statements in this paper) on average to and the fault.

Spectrum-based Bayesian reasoning can effectively rank candidate fault locations based on passing/failing test cases, but the diagnostic quality highly depends on the size and diversity of the underlying test suite. As test suites in practice often do not exhibit the necessary properties, we present a technique to extend existing test suites with new test cases that optimize the diagnostic quality. We apply probability theory concepts to guide test case generation using entropy, such that the amount of uncertainty in the diagnostic ranking is minimized. Our ENTBUG prototype extends the search-based test generation tool EVOSUITE to use entropy in the fitness function of its underlying genetic algorithm, and we applied it to seven real faults. Empirical results show that our approach reduces the entropy of the diagnostic ranking by 49% on average (compared to using the original test suite), leading to a 91% average reduction of diagnosis candidates needed to inspect to find the true faulty one.

Software (regression) testing is performed to detect errors as early as possible and guarantee that changes did not affect the system negatively. As test suites tend to grow over time, (re-)executing the entire suite becomes prohibitive. We propose an approach, RZoltar, addressing this issue: it encodes the relation between a test case and its testing requirements (code statements in this paper) in a so-called coverage matrix, maps this matrix into a set of constraints, and computes a collection of optimal minimal sets (maintaining the same coverage as the original suite) by leveraging a fast constraint solver. We show that RZoltar efficiently (0.95 seconds on average) finds a collection of test suites that significantly reduce the size (64.88% on average) maintaining the same fault detection (as initial test suite), while the well-known greedy approach needs 11.23 seconds on average to find just one solution.

Information foraging is a theory to understand how people search for information. In this theory, information scent is the perceived likelihood by the “predator” that a cue will lead to a “prey”. The better the cues, the better the information scent. In automatic debugging, it is the perceived likelihood that the diagnostic report leads to the cause of failures. In this paper, we detail a visualization, offered by the GZOLTAR toolset, that has the potential to provide better cues. With better we mean providing more information that leads to the fault than, e.g., the source code and code coverage information. The toolset provides a graphical display of the diagnostic reports yielded by well-known debugging techniques. From an information foraging point of view, we argue that the visualization is of added value while debugging. Finally, we report a user study to confirm that GZOLTAR’s visualization provides better cues for pinpointing faults.