(cache)A model and system for applying Lean Six sigma to agile software development using hybrid simulation | IEEE Conference Publication | IEEE Xplore

A model and system for applying Lean Six sigma to agile software development using hybrid simulation

Publisher: IEEE

Abstract:

Software quality control and quality assurance have close ties with predictability, speed/time, and cost of software development. Process improvement has essential impact...View more

Abstract:

Software quality control and quality assurance have close ties with predictability, speed/time, and cost of software development. Process improvement has essential impact on these factors that drive the quality of software project outcomes. While stochastic design and process improvement methodologies based on the Lean Six Sigma can greatly help with process design and improvement, software development processes are substantially different from the processes in the other disciplines such as manufacturing or service operations that produce same/similar product/services. It's not feasible to quantify software processes in a discrete manner that is required by the Six Sigma methodologies. The discrete simulation that is used in operations such as car manufacturing relies on the fact that system activities change state at discrete time points. However this cannot be applied to software development as the activities are not repetitive and they have time estimates at best. The continuous simulation approach lacks the discrete simulation advantage of identifying inefficiencies and improving the processes along the line. Then the discrete simulation has the shortcoming of detecting consequences of improvements late in the process. The model and system introduced in this paper applies Six Sigma methodologies to software processes using hybrid simulation. It uses the relatively detailed empirical data - which the lean software development and agile methodologies produce - to simulate future activities. Such predictions are used as the baseline measurement data to assess the actual results of the continuous improvement activities. The Monte Carlo simulation is used to eliminate dependency on assumption of a specific distribution function for software development activities. The System also includes a framework for collecting process data and creating the empirical knowledge base that optimizes simulation, analytics and data mining. The System collects empirical data on process...
Date of Conference: 12-15 June 2014
Date Added to IEEE Xplore: 09 October 2014
Electronic ISBN:978-1-4799-3312-9
Publisher: IEEE
Conference Location: Chicago, IL, USA

Introduction

The quality, delivery time, cost, and deliverables of software development projects are greatly impacted by the quality of software processes. There are several models and methodologies that are used to define, optimize and improve software processes. The Capability Maturity Model Integration (CMMI) by Software Engineering Institute (SEI) at Carnegie Mellon University [1] provides a framework for controlling software processes. The Software Process Improvement and Capability Determination (SPICE) by International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) called ISO/IEC 15504 [2] is a standard for software processes and their capability levels. The Personal Software Process (PSP) [3] provides guidelines for individuals while the Team Software Process (TSP) [4] provides guidelines for teams as to how to achieve quality in software development/processes and as a result quality in outcome.

References

References is not available for this document.