A self -testing approach for autonomic software

As researchers and practitioners move towards a vision of software systems that configure, optimize, protect, and heal themselves, they must also consider the implications of such self-management activities on software reliability. Autonomic computing (AC) describes a new generation of software systems that are characterized by dynamically adaptive self-management features. During dynamic adaptation, autonomic systems modify their own structure and/or behavior in response to environmental changes. Adaptation can result in new system configurations and capabilities, which need to be validated at runtime to prevent costly system failures. However, although the pioneers of AC recognize that validating autonomic systems is critical to the success of the paradigm, the architectural blueprint for AC does not provide a workflow or supporting design models for runtime testing. ^ This dissertation presents a novel approach for seamlessly integrating runtime testing into autonomic software. The approach introduces an implicit self-test feature into autonomic software by tailoring the existing self-management infrastructure to runtime testing. Autonomic self-testing facilitates activities such as test execution, code coverage analysis, timed test performance, and post-test evaluation. In addition, the approach is supported by automated testing tools, and a detailed design methodology. A case study that incorporates self-testing into three autonomic applications is also presented. The findings of the study reveal that autonomic self-testing provides a flexible approach for building safe, reliable autonomic software, while limiting the development and performance overhead through software reuse. ^

Factors associated with prompt difficulty in automated essay scoring

This study explores factors related to the prompt difficulty in Automated Essay Scoring. The sample was composed of 6,924 students. For each student, there were 1-4 essays, across 20 different writing prompts, for a total of 20,243 essays. E-rater® v.2 essay scoring engine developed by the Educational Testing Service was used to score the essays. The scoring engine employs a statistical model that incorporates 10 predictors associated with writing characteristics of which 8 were used. The Rasch partial credit analysis was applied to the scores to determine the difficulty levels of prompts. In addition, the scores were used as outcomes in the series of hierarchical linear models (HLM) in which students and prompts constituted the cross-classification levels. This methodology was used to explore the partitioning of the essay score variance.^ The results indicated significant differences in prompt difficulty levels due to genre. Descriptive prompts, as a group, were found to be more difficult than the persuasive prompts. In addition, the essay score variance was partitioned between students and prompts. The amount of the essay score variance that lies between prompts was found to be relatively small (4 to 7 percent). When the essay-level, student-level-and prompt-level predictors were included in the model, it was able to explain almost all variance that lies between prompts. Since in most high-stakes writing assessments only 1-2 prompts per students are used, the essay score variance that lies between prompts represents an undesirable or "noise" variation. Identifying factors associated with this "noise" variance may prove to be important for prompt writing and for constructing Automated Essay Scoring mechanisms for weighting prompt difficulty when assigning essay score.^