Meena Jha

Identifying Issues and Concerns in Software Reuse in Software Product Lines

One of the reasons for introducing software product lines (SPL) is the reduction of costs through reusing common assets for different products. Developing assets to be reused in different products is often not easy. Increasing complexity due to the multitude of different functions and their interactions as well as a rising number of different product variants are just some of the challenges that must be faced when reusing software and other assets. In an attempt to understand the obstacles to implementing software reuse in SPL we have conducted a survey to investigate how software reuse is adopted in SPL so as to provide the necessary degree of support for engineering software product line applications and to identify some of the issues and concerns in software reuse. This survey also gathers information from SPL practitioners on what influences the selection of software to reuse within a software product line. This paper reports the results of that survey.

Combining big data analytics with business process using reengineering

With the rise of Big Data, a data-driven approach to business is transforming enterprises. Companies today are thinking about and using data in a myriad new ways to drive business value; from reducing risk and fraud in the financial sector to bringing new pharmaceuticals to market more quickly at a higher level of efficacy. Retailers can track purchase patterns and consumer preferences more accurately to guide product and marketing strategies. Media companies can offer more accurate recommendations and create specialized promotions. Businesses of all kinds can identify new revenue opportunities and operational efficiencies. Big Data can mean different things to different organizations, but one theme remains constant: Big Data calls for a new way of thinking and combining data analytics with business process workflows. Until now businesses were limited to utilizing customer and business information contained within in-house systems. Now they are increasingly analyzing external data too, gaining new insights into customers, markets, supply chains and operations. Organisational silos and a dearth of data specialists are the main obstacles to putting big data to work effectively for decision-making. Big data analytics need to be combined with business processes to improve operations and offer innovative services to customers. Business processes need to be reengineered for big data analytics. In this paper we discuss how the combination of Big Data analytics with business process using reengineering can deliver the benefits to organizations and customers.