Computerized classification tests (CCT) have been used in high-stakes assessment settings where the express purpose of the testing is to assign a classification decision (e.g. pass/fail). One key feature of sequential probability ratio test-type procedures is that items are selected to maximize information around the cutscore region of the examinee ability distribution as opposed to common features of CATs where items are selected to maximize information at examinees' interim estimates. Previous research has examined the effectiveness of computerized adaptive tests (CAT) utilizing classification testing procedures a single cutscore as well as multiple cutscores (e.g. below basic/proficient/advanced). Several variations of the SPRT procedure have been advanced recently including a generalized likelihood ratio (GLR). While the GLR procedure has shown evidences of improved average test length while reasonably maintaining classification accuracy, it also introduces unnecessary error. The purpose of this dissertation was to propose and investigate the functionality of a modified GLR procedure which does not incorporate the unnecessary error inherent in the GLR procedure. Additionally this dissertation explored the use of the multiple cutscores and the use of ability-based item selection. This dissertation investigated the performance of three classification procedures (SPRT, GLR, and modified GLR), multiple cutscores, and two test lengths. An additional set of conditions were developed in which an ability-based item selection method was used with the modified GLR. A simulation study was performed to gather evidences of the effectiveness and efficiency of a modified GLR procedure by comparing it to the SPRT and GLR procedures. The study found that the GLR and mGLR procedures were able to yield shorter test lengths as anticipated. Additionally, the mGLR procedure using ability-based item selection produced even shorter test lengths than the cutscore-based mGLR method. Overall, the classification accuracy of the procedures were reasonably close. Examination of conditional classification accuracy in the multiple-cutscore conditions showed unexpectedly low values for each of the procedures. Implications and future research are discussed herein. / text

Software projects go through a phase of maintenance and, in case of open source projects, through hard development process. Both of these phases are prone to regressions, meaning previously working parts of system do not work anymore. To avoid this behavior, systems are being tested with long test suites, which can be sometimes time consuming. For this reason, prediction models are developed to predict software regressions using historical testing data and code changes, to detect changes that can most likely cause regression and focus testing on such parts of code. But, these predictors rely on static code analysis without deeper semantic understanding of the code. Purpose of this master thesis is to create predictor, that relies not only on static code analysis, but provides decisions based on code semantics as well.