Regression testing is a commonly used activity whose purpose is to determine whether the modifications made to a software system have introduced new faults. Textual differ-encing is a new, safe and fairly precise, selective regression testing technique that works by comparing source files from the old and the new version of the program. We have implemented the textual differencing technique in ...

Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is i...

Unswerving product quality is the main goal of any software engineering product. It involves rigorous product development and testing. Whenever new features are introduced to any existing product, the stress on quality is more, which will have more effect or impact on the ‘regression’ testing phase, in which all older functionality of the product are tested to make sure that the older functiona...

Regression test suites are developed and maintained throughout the lifetime of the software product. For testers, it is common practice to add new testcases to the existing regression test suite, with intent to test new features in the software product or to capture any newly discovered fault. Many a times the intention is to check whether the program is sufficiently tested or not. This is done...

We propose, in this paper, a hybrid regression testing technique and associated tool for object-oriented software. The technique combines, in fact, the analysis of UML models to a simple static analysis of the source code of the modified program. The basic models we use are use cases model and corresponding UML statechart and collaboration diagrams. The goal of the static analysis of the source...

The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. Logistic regression is one of the most popular supervised learning technique that is used in classification. Fields like computer vision, image analysis and engineering sciences frequently encounter data with outliers (noise). Presence of outliers in the training sampl...

Assessing the linear relationship between a set of continuous predictors and a continuous response is a well-studied problem in statistics and data mining. L2-based methods such as ordinary least squares and orthogonal regression can be used to determine this relationship. However, both of these methods become impaired when influential values are present. This problem becomes compounded when ou...