The Personal Software Process: Downscaling the factory

It is argued that the next wave of software process improvement (SPI) activities will be based on a people-centered paradigm. The most promising such paradigm, Watts Humphrey's personal software process (PSP), is summarized and its advantages are listed. The concepts of the PSP are shown also to fit a down-scaled version of Basili's experience factory. The author's data and lessons learned while practicing the PSP are presented along with personal experience, observations, and advice from the perspective of a consultant and teacher for the personal software process

Proceedings of the 19th Annual Software Engineering Workshop

The Software Engineering Laboratory (SEL) is an organization sponsored by NASA/GSFC and created to investigate the effectiveness of software engineering technologies when applied to the development of applications software. The goals of the SEL are: (1) to understand the software development process in the GSFC environment; (2) to measure the effects of various methodologies, tools, and models on this process; and (3) to identify and then to apply successful development practices. The activities, findings, and recommendations of the SEL are recorded in the Software Engineering Laboratory Series, a continuing series of reports that include this document

Lessons learned in deploying software estimation technology and tools

Developing a software product involves estimating various project parameters. This is typically done in the planning stages of the project when there is much uncertainty and very little information. Coming up with accurate estimates of effort, cost, schedule, and reliability is a critical problem faced by all software project managers. The use of estimation models and commercially available tools in conjunction with the best bottom-up estimates of software-development experts enhances the ability of a product development group to derive reasonable estimates of important project parameters. This paper describes the experience of the IBM Software Solutions (SWS) Toronto Laboratory in selecting software estimation models and tools and deploying their use to the laboratory's product development groups. It introduces the SLIM and COSTAR products, the software estimation tools selected for deployment to the product areas, and discusses the rationale for their selection. The paper also describes the mechanisms used for technology injection and tool deployment, and concludes with a discussion of important lessons learned in the technology and tool insertion process

Closing the loop on improvement: Packaging experience in the Software Engineering Laboratory

As part of its award-winning software process improvement program, the Software Engineering Laboratory (SEL) has developed an effective method for packaging organizational best practices based on real project experience into useful handbooks and training courses. This paper shares the SEL's experience over the past 12 years creating and updating software process handbooks and training courses. It provides cost models and guidelines for successful experience packaging derived from SEL experience

Experimental control in software reliability certification

There is growing interest in software 'certification', i.e., confirmation that software has performed satisfactorily under a defined certification protocol. Regulatory agencies, customers, and prospective reusers all want assurance that a defined product standard has been met. In other industries, products are typically certified under protocols in which random samples of the product are drawn, tests characteristic of operational use are applied, analytical or statistical inferences are made, and products meeting a standard are 'certified' as fit for use. A warranty statement is often issued upon satisfactory completion of a certification protocol. This paper outlines specific engineering practices that must be used to preserve the validity of the statistical certification testing protocol. The assumptions associated with a statistical experiment are given, and their implications for statistical testing of software are described

Lessons learned in transitioning to an open systems environment

Software development organizations, both commercial and governmental, are undergoing rapid change spurred by developments in the computing industry. To stay competitive, these organizations must adopt new technologies, skills, and practices quickly. Yet even for an organization with a well-developed set of software engineering models and processes, transitioning to a new technology can be expensive and risky. Current industry trends are leading away from traditional mainframe environments and toward the workstation-based, open systems world. This paper presents the experiences of software engineers on three recent projects that pioneered open systems development for NASA's Flight Dynamics Division of the Goddard Space Flight Center (GSFC)

Changes and challenges in the Software Engineering Laboratory

Since 1976, the Software Engineering Laboratory (SEL) has been dedicated to understanding and improving the way in which one NASA organization, the Flight Dynamics Division (FDD), develops, maintains, and manages complex flight dynamics systems. The SEL is composed of three member organizations: NASA/GSFC, the University of Maryland, and Computer Sciences Corporation. During the past 18 years, the SEL's overall goal has remained the same: to improve the FDD's software products and processes in a measured manner. This requires that each development and maintenance effort be viewed, in part, as a SEL experiment which examines a specific technology or builds a model of interest for use on subsequent efforts. The SEL has undertaken many technology studies while developing operational support systems for numerous NASA spacecraft missions

Leveraging object-oriented development at Ames

This paper presents lessons learned by the Software Engineering Process Group (SEPG) from results of supporting two projects at NASA Ames using an Object Oriented Rapid Prototyping (OORP) approach supported by a full featured visual development environment. Supplemental lessons learned from a large project in progress and a requirements definition are also incorporated. The paper demonstrates how productivity gains can be made by leveraging the developer with a rich development environment, correct and early requirements definition using rapid prototyping, and earlier and better effort estimation and software sizing through object-oriented methods and metrics. Although the individual elements of OO methods, RP approach and OO metrics had been used on other separate projects, the reported projects were the first integrated usage supported by a rich development environment. Overall the approach used was twice as productive (measured by hours per OO Unit) as a C++ development

Process maturity progress at Motorola Cellular Systems Division

We believe that the key success elements are related to our recognition that Software Process Improvement (SPI) can and should be organized, planned, managed, and measured as if it were a project to develop a new process, analogous to a software product. We believe that our process improvements have come as the result of these key elements: use of a rigorous, detailed requirements set (Capability Maturity Model, CMM); use of a robust, yet flexible architecture (IEEE 1074); use of a SPI project, resourced and managed like other work, to produce the specifications and implement them; and development of both internal and external goals, with metrics to support them

A quantitative comparison of corrective and perfective maintenance

This paper presents a quantitative comparison of corrective and perfective software maintenance activities. The comparison utilizes basic data collected throughout the maintenance process. The data collected are extensive and allow the impact of both types of maintenance to be quantitatively evaluated and compared. Basic statistical techniques test relationships between and among process and product data. The results show interesting similarities and important differences in both process and product characteristics