Learning Effective Changes for Software Projects

The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by "planning" about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu

SOURCE OF GROUNDWATER IRON AND MANGANESE IN CHANDRAPUR DISTRICT, CENTRAL INDIA

Groundwater sampling was carried out by grab sampling method from 36 sampling locations from the Chandrapur district in three seasons i.e. winter, summer, and post-monsoon. The samples were analysed for physicochemical parameters and heavy metals i.e. iron and manganese. Data obtained from the study area was interpreted by using multivariate statistical analysis i.e. principal component analysis, cluster analysis, correlation matrix and one way ANOVA to ascertain source apportionment of these two heavy metals. The results of the multivariate analysis revealed that iron and manganese both were associated with the lithogenic source. Groundwater irons concentration was higher when compared with manganese and at a number of sampling locations it was above the stipulated standard of BIS (Bureau of Indian Standards) (0.3 mg/L)