Chinese software companies are increasingly using open source software (OSS) components in software development. Development with OSS components faces challenges with respect to component selection, component integration, licensing compliance, and system maintenance. Although these issues have been investigated in the industry in other countries, few similar studies have been performed in China. It is therefore difficult for Chinese software companies to be aware of their special issues and to make the necessary improvements. This article describes a questionnaire-based survey of software development with OSS components in Chinese software companies. Data from 47 completed development projects in 43 companies were collected. The results show that the main motivation behind using OSS components was their modifiability and low license cost. Using a web search engine was the most common method of locating OSS components. Local acquaintance and compliance requirements were the major decisive factors in choosing a suitable component. To avoid legal exposure, the common strategy was to use components without licensing constraints. The major cost of OSS-based projects was the cost to learn and understand OSS components. Almost 84% of the components needed bug fixing or other changes to the code. However, close participation with the OSS community was rare.
Given a set ofnobjects, the objective of the 0-1 multidimensional knapsack problem (MKP_01) is to find a subset of the object set that maximizes the total profit of the objects in the subset while satisfyingmknapsack constraints. In this paper, we have proposed a new artificial bee colony (ABC) algorithm for the MKP_01. The new ABC algorithm introduces a novel communication mechanism among bees, which bases on the updating and diffusion of inductive pheromone produced by bees. In a number of experiments and comparisons, our approach obtains better quality solutions in shorter time than the ABC algorithm without the mechanism. We have also compared the solution performance of our approach against some stochastic approaches recently reported in the literature. Computational results demonstrate the superiority of the new ABC approach over all the other approaches.
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