About two decades ago, two algorithms, i.e., DDMin and QuickXPlain, for minimizing collections, were independently proposed and gained attention in the two research areas of Software Engineering and Artificial Intelligence, respectively.Whereas DDMin was developed for reducing a given test case, QuickXPlain was intended to be used for obtaining minimal conflicts efficiently.In this paper, we compare the performance of both algorithms with respect to their capabilities of minimizing collections.We found out that one algorithm outperforms the other under given prerequisites and vice versa.These findings help to select the suitable algorithm for a given task.Index Terms-test case minimization, conflict minimization, software testing, application to diagnosis and configuration

Systems exhibiting anticipatory behavior are controlling devices that are influencing decisions critical to business with increasing frequency, but testing such systems has received little attention from the artificial intelligence or software engineering communities. In this article, we describe research activities being carried out to test anticipatory systems and explore how this research contributes to the body of knowledge. In addition, we review the types of addressed anticipatory applications and point out open issues and trends. This systematic mapping study was conducted to classify and analyze the literature on testing anticipatory systems, enabling us to highlight the most relevant topics and potential gaps in this field. We identified 206 studies that contribute to the testing of systems that exhibit anticipatory behavior. The papers address testing at stages such as context sensing, inferring higher-level concepts from the sensed data, predicting the future context, and intelligent decision-making. We also identified agent testing as a trend, among others. The existing literature on testing anticipatory systems has originated from various research communities, such as those on autonomous agents and quality engineering. Although researchers have recently exhibited increasing interest in testing anticipatory systems, theoretical knowledge about testing such systems is lacking.