Software complexity has increased the need for automated software testing. Most research on automating testing, however, has focused on creating test input data. While careful selection of input data is necessary to reach faulty states in a system under test, test oracles are needed to actually detect failures. In this work, we describe Dodona, a system that supports the generation of test oracles. Dodona ranks program variables based on the interactions and dependencies observed between them during program execution. Using this ranking, Dodona proposes a set of variables to be monitored, that can be used by engineers to construct assertion-based oracles. Our empirical study of Dodona reveals that it is more effective and efficient than the current state-of-the-art approach for generating oracle data sets, and can often yield oracles that are almost as effective as oracles hand-crafted by engineers without support.

Web usage mining has proven to be an important advance for e-business systems, both by finding web user buying patterns and suggesting ways to improve web user navigation. A primary input for web usage mining is web user sessions that must be constru

Recent work in testing has demonstrated the benefits of considering test oracles in the testing process. Unfortunately, this work has focused primarily on developing techniques for generating test oracles, in particular techniques based on mutation testing. While effective for test case generation, existing research has not considered the impact of test oracles in the context of regression testing tasks. Of interest here is the problem of test case prioritization, in which a set of test cases are ordered to attempt to detect faults earlier and to improve the effectiveness of testing when the entire set cannot be executed. In this work, we propose a technique for prioritizing test cases that explicitly takes into account the impact of test oracles on the effectiveness of testing. Our technique operates by first capturing the flow of information from variable assignments to test oracles for each test case, and then prioritizing to ``cover'' variables using the shortest paths possible to a test oracle. As a result, we favor test orderings in which many variables impact the test oracle's result early in test execution. Our results demonstrate improvements in rate of fault detection relative to both random and structural coverage based prioritization techniques when applied to faulty versions of three synchronous reactive systems.

The evolution of the Web has allowed the generation of several platforms for collaborative work. One of the main contributors to these advances is the Open Source initiative, in which projects are boosted to a new level of interaction and cooperation that improves their software quality and reliability. In order to understand how the group of contributors interacts with the software under development, we propose a novel methodology that adapts Lotka-Volterra-based biological models used for host-parasite interaction. In that sense, we used the concept mutualism from social parasites. Preliminary results based on experiments on the Github collaborative platform showed that Open Source phenomena can be modeled as a mutualistic system, in terms of the evolution of the population of developers and repositories.

In this paper we propose a novel methodology for analyzing web user behavior based on session simulation by using an Ant Colony Optimization algorithm which incorporates usage, structure and content data originating from a real web site. In the first place, artificial ants learn from a clustered web user session set through the modification of a text preference vector. Then, trained ants are released through a web graph and the generated artificial sessions are compared with real usage. The main result is that the proposed model explains approximately 80% of real usage in terms of a predefined similarity measure.