The classical model of Markov decision processes with costs or rewards, while widely used to formalize optimal decision making, cannot capture scenarios where there are multiple objectives for the agent during the system evolution, but only one of these objectives gets actualized upon termination. We introduce the model of Markov decision processes with alternative objectives (MDPAO) for formalizing optimization in such scenarios. To compute the strategy to optimize the expected cost/reward upon termination, we need to figure out how to balance the values of the alternative objectives. This requires analysis of the underlying infinite-state process that tracks the accumulated values of all the objectives. While the decidability of the problem of computing the exact optimal strategy for the general model remains open, we present the following results. First, for a Markov chain with alternative objectives, the optimal expected cost/reward can be computed in polynomial-time. Second, for a single-state process with two actions and multiple objectives we show how to compute the optimal decision strategy. Third, for a process with only two alternative objectives, we present a reduction to the minimum expected accumulated reward problem for one-counter MDPs, and this leads to decidability for this case under some technical restrictions. Finally, we show that optimal cost/reward can be approximated up to a constant additive factor for the general problem.

Formal design and analysis of embedded control software relies on mathematical models of dynamical systems, and such models can be hard to obtain. In this paper, we focus on automatic construction of piecewise affine models from input-output data. Given a set of examples, where each example consists of a d-dimensional real-valued input vector mapped to a real-valued output, we want to compute a set of affine functions that covers all the data points up to a specified degree of accuracy, along with a disjoint partitioning of the space of all inputs defined using a Boolean combination of affine inequalities with one region for each of the learnt functions. While traditional machine learning algorithms such as linear regression can be adapted to learn the set of affine functions, we develop new techniques based on automatic construction of interpolants to derive precise guards defining the desired partitioning corresponding to these functions. We report on a prototype tool, Mosaic, implemented in Matlab. We evaluate its performance using some synthetic data, and compare it against known techniques using data-sets modeling electronic placement process in pick-and-place machines.

As a test suite evolves, it can accumulate redundant tests. To address this problem, many test-suite reduction techniques, based on different measures of redundancy, have been developed. A more subtle problem, that can also cause test-suite bloat and that has not been addressed by existing research, is the accumulation of similar tests. Similar tests are not redundant by any measure; but, they contain many common actions that are executed repeatedly, which over a large test suite, can degrade execution time substantially.

Mention “test case”, and it conjures up the image of a script or a program that exercises a system under test. In industrial practice, however, test cases often start out as steps described in natural language. These are essentially directions a human tester needs to follow to interact with an application, exercising a given scenario. Since tests need to be executed repeatedly, such manual tests then have to go through test automation to create scripts or programs out of them. Test automation can be expensive in programmer time. We describe a technique to automate test automation. The input to our technique is a sequence of steps written in natural language, and the output is a sequence of procedure calls with accompanying parameters that can drive the application without human intervention. The technique is based on looking at the natural language test steps as consisting of segments that describe actions on targets, except that there can be ambiguity in identifying segments, in identifying the action in a segment, as well as in the specification of the target of the action. The technique resolves this ambiguity by backtracking, until it can synthesize a successful sequence of calls. We present an evaluation of our technique on professionally created manual test cases for two open-source web applications as well as a proprietary enterprise application. Our technique could automate over 82% of the steps contained in these test cases with no human intervention, indicating that the technique can reduce the cost of test automation quite effectively.

In industrial practice, test cases often start out as steps described in natural language and are intended to be executed by a human. Since tests are executed repeatedly, they go through an automation process, in which they are converted to automated test scripts (or programs) that perform the test steps mechanically. Conventional test-automation techniques can be time-consuming, require specialized skills, and can produce fragile scripts. To address these limitations, we present a tool, called ata, for automating the test-automation task. Using a novel combination of natural-language processing, backtracking exploration, and learning, ata can significantly improve tester productivity in automating manual tests. ata also produces change-resilient scripts, which automatically adapt themselves in the presence of certain common types of user-interface changes.