Directed Test Suite Augmentation

As software evolves, engineers use regression testing to evaluate its fitness for release. Such testing typically begins with existing test cases, and many techniques have been proposed for reusing these cost-effectively. After reusing test cases, however, it is also important to consider code or behavior that has not been exercised by existing test cases and generate new test cases to validate these. This process is known as test suite augmentation. In this paper we present a directed test suite augmentation technique, that utilizes results from reuse of existing test cases together with an incremental concolic testing algorithm to augment test suites so that they are coverage-adequate for a modified program. We present results of an empirical study examining the effectiveness of our approach.

Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique

The 3PL model is a flexible and widely used tool in assessment. However, it suffers from limitations due to its need for large sample sizes. This study introduces and evaluates the efficacy of a new sample size augmentation technique called Duplicate, Erase, and Replace (DupER) Augmentation through a simulation study. Data are augmented using several variations of DupER Augmentation (based on different imputation methodologies, deletion rates, and duplication rates), analyzed in BILOG-MG 3, and results are compared to those obtained from analyzing the raw data. Additional manipulated variables include test length and sample size. Estimates are compared using seven different evaluative criteria. Results are mixed and inconclusive. DupER augmented data tend to result in larger root mean squared errors (RMSEs) and lower correlations between estimates and parameters for both item and ability parameters. However, some DupER variations produce estimates that are much less biased than those obtained from the raw data alone. For one DupER variation, it was found that DupER produced better results for low-ability simulees and worse results for those with high abilities. Findings, limitations, and recommendations for future studies are discussed. Specific recommendations for future studies include the application of Duper Augmentation (1) to empirical data, (2) with additional IRT models, and (3) the analysis of the efficacy of the procedure for different item and ability parameter distributions.