Software Cost Estimation

Software cost estimation has been a challenge for software industry for software companies for a long time. Accurate cost estimation helps software companies making money and avoiding loss of money [5].Software cost estimation can be defined as the estimated judgment of the expenses for an undertaking. Cost estimation is typically measured regarding effort. The most widely recognized metric utilized is individual months or years (or man months or years). The effort is the measure of time for one individual to work for a certain time of time. It is essential that the particular attributes of the advancement environment are requiring into record when contrasting the effort of two or more undertakings in light of the fact that no two improvement

It is an algorithmic approach to estimating the cost of a software project. By using COCOMO you can calculate the amount of effort and the time schedule for projects. From these calculations you can then find out how much staffing is required to complete a project on time. COCOMO's main metric used for calculating these values is lines of code (denoted KLOC for COCOMO II, or KDSI for COCOMO 81 and measured in thousands), function points (FP), or object points (OP). COCOMO 81 was the first form of COCOMO. It was demonstrated around programming practices of the 1980's. It has been discovered that generally speaking it has the capacity produce gauges that are inside 20% of the real values 68% of the time. COCOMO 81 has three separate models that can be utilized all through a ventures life cycle (Bohem,

As in GA and ES, PSO exploits a population of potential solutions to probe the search space. In contrast to the aforementioned methods in PSO no operators inspired by natural evolution are applied to extract a new generation of candidate solutions. Instead of mutation PSO relies on the exchange of information between individuals, called particles, of the population, called swarm. In effect, each particle adjusts its trajectory towards its own previous best position, and towards the best previous position attained by any member of its neighborhood. In the global variant of PSO, the whole swarm is considered as the neighborhood. Thus, global sharing of information takes place and particles profit from the discoveries and previous experience of all other companions during the search for promising regions of the landscape. To visualize the operation of the method consider the case of the single objective minimization case; promising regions in this case possess lower function values compared to others, visited previously [3].

ALGORITHM OF PSO
Numbers of birds are searching for food randomly in an area. There is only one piece of food is available. All birds do not know where the food is, but they know how far it is in iteration. So the best solution to find out the food is to follow the bird nearest to the food.
PSO is used for optimization problem. Each single solution in the search