Sumeet Kaur Sehra

Multi Criteria Decision Making Approach for Selecting Effort Estimation Model

Effort Estimation has always been a challenging task for the Project managers. Many researchers have tried to help them by creating different types of models. This has been already proved that none is successful for all types of projects and every type of environment. Analytic Hierarchy Process (AHP) has been identified as the tool that would help in Multi Criteria Decision Making. Researchers have identified that AHP can be used for the comparison of effort estimation of different models and techniques. But the problem with traditional AHP is its inability to deal with the imprecision and subjectivity in the pairwise comparison process. The motive of this paper is to propose Fuzzy Analytic Hierarchy Process, which can be used to rectify the subjectivity and imprecision of AHP and can be used for selecting the type of Model best suited for estimating the effort for a given problem type or environment. Instead of single crisp value, Fuzzy AHP uses a range of values to incorporate decision maker‟s uncertainty. From this range, decision maker can select the value that reflects his confidence and also he can specify his attitude like optimistic, pessimistic or moderate. In this work, the comparison of AHP and Fuzzy AHP is concluded using a case study of selection of effort estimation model.

Particle swarm optimization based effort estimation using Function Point analysis

Software development effort estimation is one of the most important activities in software project management. Various models have been proposed to construct a relationship between software size and effort, however there are many problems. This is because project data, available in the initial stages of project is often incomplete, inconsistent, uncertain and unclear. Accurate estimation of the software effort and schedule affects the budget computation. Inaccurate estimates lead to failure of obtaining a profit, increased probability of project incompletion and delay of the project delivery date. Function Points (FP) are one of the size metrics which are used for estimating the effort of the project and Particle Swarm Optimization (PSO), a swarm intelligence technique is used to tune the parameters of Value Adjustment Factor (VAF) which is used to obtain the function count From this optimized function count, optimized Albrecht & Gaffney effort is estimated. The estimated effort is compared with the existing effort models and performance analysis is done on the basis of %MARE and RMSE. The research shows that the results of the proposed model are far better than the existing models.

A framework for software quality model selection using TOPSIS

Software and its quality is the key deciding factor about success and failure of a business. To give a check to quality of software, software quality models have been introduced to identify the type of software products and to find scope of it. Multi criteria decision-making technique has been an interesting topic to compare software quality model and then to find the best one by using different parameters. Multi Criteria Decision Making is used to select particular alternative based on different criterion. In this paper, TOPSIS technique one of the method of multi criteria decision making has been used to select the best software quality model. TOPSIS method ranking process, simplicity and precise result makes it above all the other approaches already been used. This framework selects the best model according to the criterion reliability, efficiency and maintenance. The values for these models have been calculated and ranked according to maximum value as 1 rank.

Analysis of Data Mining Techniques for Software Effort Estimation

Software effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, datamining is used to improve an organizations software process quality, e.g. the accuracy of effort estimations. There are a large number of different method combination exists for software effort estimation, selecting the most suitable combination becomes the subject of research in this paper. In this study data preprocessing is implemented and effort is calculated using COCOMO Model. Then data mining techniques OLS Regression and K Means Clustering are implemented on preprocessed data and results obtained are compared and data mining techniques when implemented on preprocessed data proves to be more accurate then OLS Regression Technique.

Fuzzy Multi Criteria Approach for Selecting Software Quality Model

Software Quality models have been proposed to evaluate general and definite type of software products. These models were proposed to evaluate scope of software product. There has been an increasing interest in recent times for using Multi Criteria Decision making techniques to present the comparison of Software Quality models. Earlier Analytic Hierarchy Process (AHP) has been used by researchers. The use of Fuzzy Prioritization Method for this offers several advantages when compared to other commonly used techniques. In Fuzzy Analytic Hierarchy Process elements of the group pairwise comparison matrices are presented as fuzzy numbers in order to model uncertainty and imprecision in the Decision Maker’s (DM) judgments. In this paper Fuzzy AHP is concluded with study of selection of Software Quality model.

Effect of Data Preprocessing on Software Effort Estimation

Software effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, data mining is used to improve an organizations software process quality, e. g. the accuracy of effort estimations . There are a large number of different method combination exists for software effort estimation, selecting the most suitable combination becomes the subject of research in this paper. In this study, three simple preprocessors are taken (none, norm, log) and effort is measured using COCOMO model. Then results obtained from different preprocessors are compared and norm preprocessor proves to be more accurate as compared to other preprocessors.

A systematic review of applications of Bee Colony Optimization

In this paper an overview of Bee Colony Optimization and area of its application where it has been used is given. Bee Colony Optimization is based on concept of Swarm Intelligence (SI), the artificial intelligence (AI) which is based on decentralized and self-organizing systems that can either be natural or artificial. Bee Colony Optimization is a meta-heuristic algorithm which uses the swarm behavior of bees to interact locally with one another in their environment that simulates the foraging behavior of honey bees and combines the global explorative search with local explorative search. The Bees Algorithms hunts synchronously the most promising regions of the solution space and also samples the most favorable regions. BCO is a class of optimization algorithm which uses the bottom-up approach of modeling and swarm intelligence of honeybees. The primary aim of this paper is to give an insight into the areas in which BCO can be used.

SpatialHadoop: For OpenStreetMap data

With the change of time information related to geography and volunteered geography also changes. In this way extraction of spatial patterns from crowdsourced data has become most valuable for service suppliers. These patterns represent the spatial features of the co-related objects. The existing approaches used Dijkstras algorithm and Euclidean distance to find spatial patterns which can not compute accurately. Crowdsourced data is growing on daily basis through mobile phones, road networks and remote sensors. In order to process this type of large data set is also becoming difficult. In this research work we have proposed a system to process crowdsourced data taken from OpenStreetMap to mine the useful patterns using SpatialHadoop. SpatialHadoop has used Pigeon script, a spatial extension to Pig that is a high level language. These patterns will assist service providers to offer different sites based on facilities. In this extraction method, spatial data is loaded into the system and filtered for nodes, ways and relations. The filtered data is used for the mining process by using kNN joins. After this the evaluation of multiple resolution pruning filter with spatial datasets are generated using argument values. The different data sets have been checked using this methodology to extract the spatial patterns. This technique is compared with PostgreSQL and it is observed that SDM has provided more efficient results. The result obtained from experiment has shown the performance of our system that is better in comparison to the already existing systems in consideration of efficiency, speed and accuracy that rely on a network.

Review of Different Queuing Disciplines in VOIP, Video Conferencing and File Transfer

Now a day Internet only put up best effort service. Traffic is transmitting as earliest as possible, but during transmission, there is no guarantee of timelines or real delivery of packets. With the swiftly transformation of the Internet into a commercial infrastructure, demands for a quality of service have developed in rapid rate. People of the present world are very much depending upon the various network services like VOIP, Video conferencing and File Transfer (6,7). Various categories of Traffic Management systems are used in those services. Queuing is one of the very important mechanisms in traffic management system. Each router in the network must implement some queuing discipline that control how packets are buffered while waiting to be transmitted. The main aim of this paper is to highlight quality of service (QoS) analysis using different queuing disciplines.