Kyung-A Yoon

An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method

Estimation of development effort without imposing overhead on the project and the development team is of paramount importance for any software company. This study proposes a new effort estimation methodology aimed at agile and iterative development environments not suitable for description by traditional prediction methods. We propose a detailed development methodology, discuss a number of architectures of such models (including a wealth of augmented regression models and neural networks) and include a thorough case study of Extreme Programming (XP) in two semi-industrial projects. The results of this research evidence that in the XP environment under study the proposed incremental model outperforms traditional estimation techniques most notably in early phases of development. Moreover, when dealing with new projects, the incremental model can be developed from scratch without resorting itself to historic data.The quality of software measurement data affects the accuracy of project managers decision making using estimation or prediction models and the understanding of real project status. During the software measurement implementation, the outlier which reduces the data quality is collected, however its detection is not easy. To cope with this problem, we propose an approach to outlier detection of software measurement data using the k-means clustering method in this work.

An empirical analysis of software effort estimation with outlier elimination

Accurate software effort estimation has always been challenge for software engineering communities. To improve the estimation accuracy of software effort, many studies have focused on effort estimation methods without any consideration of data quality, although data quality is one of important factors to impact to the estimation accuracy. In this paper, we investigate the influence of outlier elimination upon the accuracy of software effort estimation through empirical studies applying two outlier elimination methods(Least trimmed square and K-means clustering) and three effort estimation methods( Least squares, Neural network and Bayesian network) associatively. The empirical studies are performed using two industry data sets(the ISBSG Release 9 and the Bank data set which consists of the project data performed in a bank in Korea) with or without outlier elimination.

Filtering of Inconsistent Software Project Data for Analogy-Based Effort Estimation

Accurate software effort estimation is essential for successful project management. To improve the accuracy, a number of estimation techniques have been developed. Among those, Analogy-Based Estimation (ABE) has become one of the mainstreams of effort estimation. In general, ABE infers the effort to accomplish a new project from the efforts of the historical projects which possess similar characteristics. ABE is simple, yet it can be affected by the noise in historical projects. Noise is generally the data corruptions which may cause negative affect on the performance of a model built on the historical data. In this study, we propose an approach to filtering noise in the historical projects to improve the accuracy of ABE. We introduce and measure the Effort-Inconsistency Degree (EID), the degree that the effort of a historical project is inconsistent from those of its similar projects. Based on EID, we identify and filter the noise in terms of the inconsistent historical project data. We have validated the performance of ABE with our approach and three representative filtering techniques, namely the Edited Nearest Neighbor algorithm, the Univariate Outlier Elimination, and the Genetic Algorithm based project selection, on three software project datasets (Desharnais, Maxwell, and ISBSG (International Software Benchmarking Standards Group) Telecom). The experimental results suggest that our approach can improve the accuracy of ABE more effectively than can the other approaches.

Improving the Accuracy of Software Effort Estimation Based on Multiple Least Square Regression Models by Estimation Error-Based Data Partitioning

Accurate software effort estimation is one of the key factors to a successful project by making a better software project plan. To improve the estimation accuracy of software effort, many studies usually aimed at proposing novel effort estimation methods or combining several approaches of the existing effort estimation methods. However, those researches did not consider the distribution of historical software project data which is an important part impacting to the effort estimation accuracy. In this paper, to improve effort estimation accuracy by least squares regression, we propose a data partitioning method by the accuracy measures, MRE and MER which are usually used to measure the effort estimation accuracy. Furthermore, the empirical experimentations are performed by using two industry data sets (the ISBSG Release 9 and the Bank data set which consists of the project data performed in a bank in Korea).

Deriving Software Process Simulation Model from SPEM-based Software Process Model

It is difficult to adopt a simulation technology for simulating a software process because of the difficulty in developing a simulation model. In order to resolve the difficulty, we consider the following issues: reducing the cost to develop a simulation model, reducing the simulation model complexity, and increasing the modularity of a simulation model. We propose an approach to deriving a discrete event system specification (DEVS)-Hybrid simulation model from a software process engineering meta-model (SPEM)-based software process model. We provide the mapping between the elements of SPEM and the DEVS-Hybrid formalism and the transformation rules for automatically deriving a simulation model from a descriptive process model. Our approach resolves the issues by the transformation rules and the hierarchical and modularized modeling properties of UML and DEVS.

Model-Based Project Process Analysis Using Project Tracking Data

In software process improvement, accumulating and analyzing the historical data from past projects are essential work. However, setting up the systematic and logical measurement and analysis program is very difficult. Many mature organizations have their own measurement program for the process improvement. However, most of them are based on the statistical metrics-driven approach that consequently limits logical reasoning on the detailed analysis on the process. In this paper, we propose a process analysis approach called MPAF(Model-based Process Analysis Framework), based on formal process modeling. In MPAF, the corresponding formal process instance model is recovered through data gathering from a project execution. Various formal analysis can be performed on the recovered and reconstructed process instance model for diagnosing the vitality of the project. We also performed experimental case study by applying MPAF to real world industry projects.