Ada software productivity prototypes: A case study

A case study of the impact of Ada on a Command and Control project completed at the Jet Propulsion Laboratory (JPL) is given. The data for this study was collected as part of a general survey of software costs and productivity at JPL and other NASA sites. The task analyzed is a successful example of the use of rapid prototyping as applied to command and control for the U.S. Air Force and provides the U.S. Air Force Military Airlift Command with the ability to track aircraft, air crews and payloads worldwide. The task consists of a replicated database at several globally distributed sites. The local databases at each site can be updated within seconds after changes are entered at any one site. The system must be able to handle up to 400,000 activities per day. There are currently seven sites, each with a local area network of computers and a variety of user displays; the local area networks are tied together into a single wide area network. Using data obtained for eight modules, totaling approximately 500,000 source lines of code, researchers analyze the differences in productivities between subtasks. Factors considered are percentage of Ada used in coding, years of programmer experience, and the use of Ada tools and modern programming practices. The principle findings are the following. Productivity is very sensitive to programmer experience. The use of Ada software tools and the use of modern programming practices are important; without such use Ada is just a large complex language which can cause productivity to decrease. The impact of Ada on development effort phases is consistent with earlier reports at the project level but not at the module level

Source Lines Counter (SLiC) Version 4.0

Source Lines Counter (SLiC) is a software utility designed to measure software source code size using logical source statements and other common measures for 22 of the programming languages commonly used at NASA and the aerospace industry. Such metrics can be used in a wide variety of applications, from parametric cost estimation to software defect analysis. SLiC has a variety of unique features such as automatic code search, automatic file detection, hierarchical directory totals, and spreadsheet-compatible output. SLiC was written for extensibility; new programming language support can be added with minimal effort in a short amount of time. SLiC runs on a variety of platforms including UNIX, Windows, and Mac OSX. Its straightforward command-line interface allows for customization and incorporation into the software build process for tracking development metrics.

Software Development Standard Processes (SDSP)

A JPL-created set of standard processes is to be used throughout the lifecycle of software development. These SDSPs cover a range of activities, from management and engineering activities, to assurance and support activities. These processes must be applied to software tasks per a prescribed set of procedures. JPL s Software Quality Improvement Project is currently working at the behest of the JPL Software Process Owner to ensure that all applicable software tasks follow these procedures. The SDSPs are captured as a set of 22 standards in JPL s software process domain. They were developed in-house at JPL by a number of Subject Matter Experts (SMEs) residing primarily within the Engineering and Science Directorate, but also from the Business Operations Directorate and Safety and Mission Success Directorate. These practices include not only currently performed best practices, but also JPL-desired future practices in key thrust areas like software architecting and software reuse analysis. Additionally, these SDSPs conform to many standards and requirements to which JPL projects are beholden

Introduction to Costing

No abstract availabl

A Note on a One-sided, Chebychev-type Inequality

economics, statistics, Agricultural and Resource Economics

Software Development Cost Estimation Executive Summary

Identify simple fully validated cost models that provide estimation uncertainty with cost estimate. Based on COCOMO variable set. Use machine learning techniques to determine: a) Minimum number of cost drivers required for NASA domain based cost models; b) Minimum number of data records required and c) Estimation Uncertainty. Build a repository of software cost estimation information. Coordinating tool development and data collection with: a) Tasks funded by PA&E Cost Analysis; b) IV&V Effort Estimation Task and c) NASA SEPG activities