Practical Experiences in using Model-Driven Engineering to Develop Trustworthy Computing Systems

In this paper, we describe how Motorola has deployed model-driven engineering in product development, in particular for the development of trustworthy and highly reliable telecommunications systems, and outline the benefits obtained. Model-driven engineering has dramatically increased both the quality and the reliability of software developed in our organization, as well as the productivity of our software engineers. Our experience demonstrates that model-driven engineering significantly improves the development process for trustworthy computing systems

Software Engineering Development Environment For The Launch Processing System

Tasked with supporting a progressive Shuttle launch rate, Lockheed Engineering and Software Production set out in 1984 to address the need to increase software productivity. Attention was focused on innovative tools since existing computer development systems were being reallocated for Shuttle operational testing and launch activities. It became apparent that due to the highly integrated nature of software production activities, a solution involving a local area network of engineering workstations was required. After prototyping and proving the design for increasing productivity, Lockheed procured and installed a networked computing system which generated a state-of-the-art environment for software engineering. The introduction of this new technology not only brought about new methods of implementing software changes, it resulted in a culture change for nearly everyone involved in the development cycle

Manpower and project planning

The purpose was to study how manpower and projects are planned at the Facilities Engineering Division (FENGD) within the Systems Engineering and Operations Directorate of the LaRC and to make recommendations for improving the effectiveness and productivity ot the tools that are used. The existing manpower and project planning processes (including the management plan for the FENGD, existing manpower planning reports, project reporting to LaRC and NASA Headquarters, employee time reporting, financial reporting, and coordination/tracking reports for procurement) were discussed with several people, and project planning software was evaluated

Software engineering methodologies and tools

Over the years many engineering disciplines have developed, including chemical, electronic, etc. Common to all engineering disciplines is the use of rigor, models, metrics, and predefined methodologies. Recently, a new engineering discipline has appeared on the scene, called software engineering. For over thirty years computer software has been developed and the track record has not been good. Software development projects often miss schedules, are over budget, do not give the user what is wanted, and produce defects. One estimate is there are one to three defects per 1000 lines of deployed code. More and more systems are requiring larger and more complex software for support. As this requirement grows, the software development problems grow exponentially. It is believed that software quality can be improved by applying engineering principles. Another compelling reason to bring the engineering disciplines to software development is productivity. It has been estimated that productivity of producing software has only increased one to two percent a year in the last thirty years. Ironically, the computer and its software have contributed significantly to the industry-wide productivity, but computer professionals have done a poor job of using the computer to do their job. Engineering disciplines and methodologies are now emerging supported by software tools that address the problems of software development. This paper addresses some of the current software engineering methodologies as a backdrop for the general evaluation of computer assisted software engineering (CASE) tools from actual installation of and experimentation with some specific tools

The development and technology transfer of software engineering technology at NASA. Johnson Space Center

The United State's big space projects of the next decades, such as Space Station and the Human Exploration Initiative, will need the development of many millions of lines of mission critical software. NASA-Johnson (JSC) is identifying and developing some of the Computer Aided Software Engineering (CASE) technology that NASA will need to build these future software systems. The goal is to improve the quality and the productivity of large software development projects. New trends are outlined in CASE technology and how the Software Technology Branch (STB) at JSC is endeavoring to provide some of these CASE solutions for NASA is described. Key software technology components include knowledge-based systems, software reusability, user interface technology, reengineering environments, management systems for the software development process, software cost models, repository technology, and open, integrated CASE environment frameworks. The paper presents the status and long-term expectations for CASE products. The STB's Reengineering Application Project (REAP), Advanced Software Development Workstation (ASDW) project, and software development cost model (COSTMODL) project are then discussed. Some of the general difficulties of technology transfer are introduced, and a process developed by STB for CASE technology insertion is described

Estimation of Defect proneness Using Design complexity Measurements in Object- Oriented Software

Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and methods which can enable a more reusable, reliable, easily maintainable and high quality software systems with deeper control on software generation process. Quality and productivity are indeed the two most important parameters for controlling any industrial process. Implementation of a successful control system requires some means of measurement. Software metrics play an important role in the management aspects of the software development process such as better planning, assessment of improvements, resource allocation and reduction of unpredictability. The process involving early detection of potential problems, productivity evaluation and evaluating external quality factors such as reusability, maintainability, defect proneness and complexity are of utmost importance. Here we discuss the application of CK metrics and estimation model to predict the external quality parameters for optimizing the design process and production process for desired levels of quality. Estimation of defect-proneness in object-oriented system at design level is developed using a novel methodology where models of relationship between CK metrics and defect-proneness index is achieved. A multifunctional estimation approach captures the correlation between CK metrics and defect proneness level of software modules.Comment: 5 pages, 1 figur

A knowledge based software engineering environment testbed

The Carnegie Group Incorporated and Boeing Computer Services Company are developing a testbed which will provide a framework for integrating conventional software engineering tools with Artifical Intelligence (AI) tools to promote automation and productivity. The emphasis is on the transfer of AI technology to the software development process. Experiments relate to AI issues such as scaling up, inference, and knowledge representation. In its first year, the project has created a model of software development by representing software activities; developed a module representation formalism to specify the behavior and structure of software objects; integrated the model with the formalism to identify shared representation and inheritance mechanisms; demonstrated object programming by writing procedures and applying them to software objects; used data-directed and goal-directed reasoning to, respectively, infer the cause of bugs and evaluate the appropriateness of a configuration; and demonstrated knowledge-based graphics. Future plans include introduction of knowledge-based systems for rapid prototyping or rescheduling; natural language interfaces; blackboard architecture; and distributed processin