Mathew, George | Search NASA

NASA NTRS2017

The NASA Analogy Software Cost Model: A Web-Based Cost Analysis Tool

This paper provides an overview of the many new features and algorithm updates in the release of the NASA Analogy Software Cost Tool (ASCoT). ASCoT is a web-based tool that provides a suite of estimation tools to support early lifecycle NASA Flight Software analysis. ASCoT employs advanced statistical methods such as Cluster Analysis to provide an analogy based estimate of software delivered lines of code and development effort, a regression based Cost Estimating Relationships (CER) model that estimates cost (dollars), and a COCOMO II based estimate. The ASCoT algorithms are designed to primarily work with system level inputs such as mission type (earth orbiter vs. planetary vs. rover), the number of instruments, and total mission cost. This allows the user to supply a minimal number of mission-level parameters which are better understood early in the life-cycle, rather than a large number of complex inputs.

Hihn, Jairus↗

NASA NTRS2015

NASA Software Cost Estimation Model: An Analogy Based Estimation Model

The cost estimation of software development activities is increasingly critical for large scale integrated projects such as those at DOD and NASA especially as the software systems become larger and more complex. As an example MSL (Mars Scientific Laboratory) developed at the Jet Propulsion Laboratory launched with over 2 million lines of code making it the largest robotic spacecraft ever flown (Based on the size of the software). Software development activities are also notorious for their cost growth, with NASA flight software averaging over 50% cost growth. All across the agency, estimators and analysts are increasingly being tasked to develop reliable cost estimates in support of program planning and execution. While there has been extensive work on improving parametric methods there is very little focus on the use of models based on analogy and clustering algorithms. In this paper we summarize our findings on effort/cost model estimation and model development based on ten years of software effort estimation research using data mining and machine learning methods to develop estimation models based on analogy and clustering. The NASA Software Cost Model performance is evaluated by comparing it to COCOMO II, linear regression, and K- nearest neighbor prediction model performance on the same data set.

Hihn, Jairus↗