A Tool for Assisted Correction of Programming Exercises in Java Based in Computational Reflection

INTRODUCTION: This work reports on the creation and use of a tool to verify compliance in java programming exercises. The solution is based on the hypothesis that computational reflection can provide a way to automatically assess the programing competences of students. The work reflects the concern to make students learning a programming language have practical activities in parallel to what they learn in theoretical classes. OBJECTIVE: Attesting the effectiveness of using computational reflection to automatically correct programming exercises. Provide the teacher with a tool to support the follow-up of practical activities. Provide students with immediate feedback on their learning, so as to encourage them to behave more autonomously. METHOD: A case study was carried out with two classes of a computer sciencecourse. They answered five practical programming exercices and their responses for each activity were collected in source code format, which were used as the basis of solutions, totaling 100 responses.A comparative analysis was made between the notes obtained through CodeTeacher and the notes assigned by a group of teachers. RESULTS: Comparing the expected notes and the actual notes, the automatic correction obtained an accuracy of 90.17%. CONCLUSION: The use of computational reflection techniques for assisted correction in programming classes can bring beneficial result. Teachers can optimize their work and have performance reports of their students. Students can also be benefited by having an immediate feedback, so they can perceive themselves capable of achieving the learning objectives defined by the teacher.

Towards Dynamic Evolution of Runtime Variability Based on Computational Reflection

Given the frequently changing nature of the user requirements and environments in software systems, runtime variability in today’s software systems should be capable of evolving during execution. Computational reflection is required to facilitate accessing and customizing runtime variability during this evolution process. However, realizing this computational reflection includes various practical complexities since the runtime variability is typically neither explicitly represented in software systems nor changeable during runtime. To address this problem, this paper proposes a software architecture to support computational reflection of runtime variability, along with a corresponding causal-connection mechanism to realize the introspection and intercession (i.e. representing runtime variability model, and adding, removing, replacing variability elements and their relations). The proposed software architecture consists of a meta level that represents runtime variability model using objectification, and a base level that organizes and manipulates the implementation of variability elements via reconfiguration. The causal-connection mechanism integrated in our proposed model is designed to synchronize the representation and the implementation. Further, we developed a Reflective Runtime Variability Framework (R2VF) to support the development and operation of the systems with the reflection of runtime variability. The effectiveness and applicability of our approach has been evaluated by applying R2VF to Personal Data Resource Network.

A Computational Reflection Based Distributed Query Model

Distributed software systems often intertwine the handling of application functionalities with distribution related issues including location transparent access to services, workload allocation among different hosts, adaptation to changing conditions of the network. In the paper, a computational reflection based distributed query model is proposed. The model uses computational reflection to divide the distributed query system into two levels: the meta level and the base level. Meta objects that existed in the meta level are responsible for control and description of the base level, hence the state change of the base level could be captured by the meta level and be controlled according to the meta level’s reaction. The advantages of the model are that adapt different changes such as data resources in the network, network conditions, query plan, and provide the capability of fault tolerance.