Program Annotations: Feedback for Students Learning to Program

This paper presents a novel program annotation mechanism which enables students to obtain feedback from tutors on their programs in a far simpler and more efficient way than is possible with, for example, email. A common scenario with beginning students is to email tutors with copies of their malfunctioning programs. Unfortunately the emailed program often bears little resemblance to the program the student has been trying to make work; often it is incomplete, a different version and corrupted. We propose an annotation mechanism enabling students to simply and easily annotate their programs with comments asking for help. Similarly our mechanism enables tutors to view students’ programs and to reply to their comments in a simple and structured fashion. This means students can get frequent and timely feedback on their programs; tutors can provide such feedback efficiently, and hence students’ learning is greatly improved.

Towards model round-trip engineering : an abductive approach

Providing support for reversible transformations as a basis for round-trip engineering is a significant challenge in model transformation research. While there are a number of current approaches, they require the underlying transformation to exhibit an injective behaviour when reversing changes. This however, does not serve all practical transformations well. In this paper, we present a novel approach to round-trip engineering that does not place restrictions on the nature of the underlying transformation. Based on abductive logic programming, it allows us to compute a set of legitimate source changes that equate to a given change to the target model. Encouraging results are derived from an initial prototype that supports most concepts of the Tefkat transformation language

Topological SLAM using neighbourhood information of places

Perceptual aliasing makes topological navigation a difficult task. In this paper we present a general approach for topological SLAM~(simultaneous localisation and mapping) which does not require motion or odometry information but only a sequence of noisy measurements from visited places. We propose a particle filtering technique for topological SLAM which relies on a method for disambiguating places which appear indistinguishable using neighbourhood information extracted from the sequence of observations. The algorithm aims to induce a small topological map which is consistent with the observations and simultaneously estimate the location of the robot. The proposed approach is evaluated using a data set of sonar measurements from an indoor environment which contains several similar places. It is demonstrated that our approach is capable of dealing with severe ambiguities and, and that it infers a small map in terms of vertices which is consistent with the sequence of observations.