In the modern era of technology, many software systems are legacy software systems that have become too expensive to replace and too costly to re-engineer. Considering the need of improving the business values of such systems is the need to do so in the simplest, cost-effective, and least risky way available. There are a number of approaches used by software engineers to mitigate legacy systems by inserting new computing techniques. A relatively recent technique in software engineering concentrate on implementing self-managing attributes, like autonomic behavior in such legacy software and systems of applications in order to avoid the need for human monitoring and intervention. In this paper survey of different techniques and frameworks for building autonomous system have been presented. These techniques considerably reduces the cost of maintaining legacy systems.

Investing in stock market has been one of the most profitable options for people since long time as it yields high returns. But it is associated with a greater risk because stock price movements of a particular company depend on various factors such as market position, world economy, country's economy, company's growth, performance, etc. Hence, it requires a profound knowledge and a complete understanding of the company stocks, its performance, the global scenario, etc. for making safe investment decisions. In this context, many algorithms have been proposed and tools have been developed using which the stock market data has been analyzed and stock price movements have been predicted. In this paper an approach using Artificial Neural Network for classifying the companies based on the fundamental analysis of key financial ratios is presented. The result of this classification can then be used for ranking the companies for predicting the stock price in Indian market across different business sectors.

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