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Context: Evolutionary algorithms typically require large number of evaluations (of solutions) to reach their conclusions – which can be very slow and expensive to evaluate. Objective: To solve search-based SE problems, using fewer evaluations than evolutionary methods. Method: Instead of mutating a small population, we build a very large initial population(More)
Teaching has always been a face to face interaction and hence requires physical presence of both teacher and the student. Teleteaching has been a revolution since it introduces teaching to geographic independence hence is a distributed system. Teleteaching as in any collaborative environment is very difficult to design. Before any implementation we need to(More)
Finding the optimally performing configuration of a software system for a given setting is often challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, building an accurate performance model can be very expensive (and is often infeasible in practice). The central insight of this(More)
Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations. Prior work on predicting the performance of software configurations suffered from either (a) requiring far too many sample(More)
Context: One of the black arts of data mining is learning the magic parameters that control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution(DE), have been proposed to learn those parameters. They’ve been proved to be able to improve learner performance. Objective: We want to(More)
Increasingly, SE researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. An alternative approach, proposed in this paper, is to start with a very large(More)
Most problems in search-based software engineering involves balancing conflicting objectives. Prior approaches to this task have required a large number of evaluations– making them very slow to execute and very hard to comprehend. To solve these problems, this paper introduces FLASH, a decisiontree based optimizer that incrementally grows one decision tree(More)
Classroom teaching has always been a face to face interaction between students and teachers and here in the proposed system we would like to preserve this aspect of teaching with help of information and communication technology. We like to bring this quality along with the concept of geographical independence in a distributed system. Teleteaching as in any(More)
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