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This paper describes the application of formal methods and object-oriented modeling to reverse engineering, in which formal specifications are developed for existing, or legacy, code.
A validation procedure for the Ada binding of the Graphical Kernel System (GKS) is being developed. PRIOR Data Sciences is also producing a version of the GKS written in Ada. These major software engineering projects will provide an opportunity to demonstrate a sound approach for software testing in an Ada environment. The GKS/Ada validation capability will be a collection of test programs and data, and test management guidelines. These products will be used to assess the correctness, completeness, and efficiency of any GKS/Ada implementation. The GKS/Ada developers will be able to obtain the validation software for their own use. It is anticipated that this validation software will eventually be taken over by an independent standards body to provide objective assessments of GKS/Ada implementations, using an approach similar to the validation testing currently applied to Ada compilers. In the meantime, if requested, this validation software will be used to assess GKS/Ada products. The second project, implementation of GKS using the Ada language, is a conventional software engineering tasks. It represents a large body of Ada code and has some interesting testing problems associated with automatic testing of graphics routines. Here the normal test practices which include automated regression testing, independent quality assistance, test configuration management, and the application of software quality metrics will be employed. The software testing methods emphasize quality enhancement and automated procedures. Ada makes some aspects of testing easier, and introduces some concerns. These issues are addressed.
Recognizing the importance of building software with the robustness to accommodate rapid advances in technology, developers have focused on methods which preserve both past and future investment in software and in providing an advanced software engineering environment that frees the engineer, to the greatest extent possible, from the more routine aspects of software design and development. The software engineer is permitted to concentrate on the creative aspects of problem resolution. Standard languages such as Ada maximize portability across hardware and operating systems. Standard interfaces which enhance portability and permit the incorporation of new technology as it becomes available have been developed. Software design and development techniques which maximize portability receive increasing emphasis. Experience gained in porting an Ada application between two widely varying environments is evaluated in light of current practices to maximize software portability.
Various software testing techniques are described. The techniques are classified as dynamic or static, structural or functional, and manual or automated. The objects tested include the elements designed during the development of the software, such as codes, data structures, and requirements. Testing techniques and procedures applicable to each phase of software development are examined; the development phases are: software requirements analysis, preliminary design, detailed design, coding, testing, and operation and maintenance. The characteristics of a future software engineering environment for software testing and validation are discussed.
MountainNet is a small firm which serves as a distribution center for AdaNET Services. These services include providing host systems and telecommunications for AdaNET, developing and supporting AdaNET Information Services, and developing a dynamic software inventory. AdaNET hosts are a Data General MV8000II and DEC VAX. Telecommunications are provided via the MountainNet private network and the Telenet public access dial network. Initially, the AdaNET Information Services will include software repositories, user communications and forums, software engineering information, bibliographic and library services, and an educational directory. The dynamic software inventory, which will become available in January 1990, will contain software engineering code and parts.
A software tool was developed for predicting the size of a future computer program at an early stage in its development. The system is intended to enable a user who is not expert in Software Engineering to estimate software size in lines of source code with an accuracy similar to that of an expert, based on the program's functional specifications. The project was planned as a knowledge based system with a field prototype as the goal of Phase 2 and a commercial system planned for Phase 3. The researchers used techniques from Artificial Intelligence and knowledge from human experts and existing software from NASA's COSMIC database. They devised a classification scheme for the software specifications, and a small set of generic software components that represent complexity and apply to large classes of programs. The specifications are converted to generic components by a set of rules and the generic components are input to a nonlinear sizing function which makes the final prediction. The system developed for this project predicted code sizes from the database with a bias factor of 1.06 and a fluctuation factor of 1.77, an accuracy similar to that of human experts but without their significant optimistic bias.
The activities of a field test site for the Software Engineering Institute's software process definition project are discussed. Products tested included the improvement model itself, descriptive modeling techniques, the CMM level 2 framework document, and the use of process definition guidelines and templates. The software process improvement model represents a five stage cyclic approach for organizational process improvement. The cycles consist of the initiating, diagnosing, establishing, acting, and leveraging phases.
The Program Development Environment (PDE) is a tool for massively parallel programming of distributed-memory architectures. Adopting a knowledge-based approach, the PDE eliminates the complexity introduced by parallel hardware with distributed memory and offers complete transparency in respect of parallelism exploitation. The knowledge-based part of the PDE is realized in CLIPS. Its principal task is to find an efficient parallel realization of the application specified by the user in a comfortable, abstract, domain-oriented formalism. A large collection of fine-grain parallel algorithmic skeletons, represented as COOL objects in a tree hierarchy, contains the algorithmic knowledge. A hybrid knowledge base with rule modules and procedural parts, encoding expertise about application domain, parallel programming, software engineering, and parallel hardware, enables a high degree of automation in the software development process. In this paper, important aspects of the implementation of the PDE using CLIPS and COOL are shown, including the embedding of CLIPS with C++-based parts of the PDE. The appropriateness of the chosen approach and of the CLIPS language for knowledge-based software engineering are discussed.
NASA's Software Engineering Laboratory (SEL), one of the earliest pioneers in the areas of software process improvement and measurement, has had a significant impact on the software business at NASA Goddard. At the heart of the SEL's improvement program is a belief that software products can be improved by optimizing the software engineering process used to develop them and a long-term improvement strategy that facilitates small incremental improvements that accumulate into significant gains. As a result of its efforts, the SEL has incrementally reduced development costs by 60%, decreased error rates by 85%, and reduced cycle time by 25%. In this paper, we analyze the SEL's experiences on three major improvement initiatives to better understand the cyclic nature of the improvement process and to understand why some improvements take much longer than others.
Safety standards contain technical and process-oriented safely requirements. The best time to include these requirements is early in the development lifecycle of the system. When software safety requirements are levied on a legacy system after the fact, a retrospective safety case will need to be constructed for the software in the system. This can be a difficult task because there may be few to no art facts available to show compliance to the software safely requirements. The risks associated with not meeting safely requirements in a legacy safely-critical computer system must be addressed to give confidence for reuse. This paper introduces a proposal for a software safely risk taxonomy for legacy safely-critical computer systems, by specializing the Software Engineering Institute's 'Software Development Risk Taxonomy' with safely elements and attributes.
The premise that object-oriented technology (OOT) is the most significant technology ever examined by the Software Engineering Laboratory is examined. The evolution of the use of OOT in the Software Engineering Laboratory (SEL) 'experience factory' is described in terms of the SEL's original expectations, focusing on how successive generations of projects have used OOT. General conclusions are drawn on how the usage of the technology has evolved in this environment.
This paper discusses return on investment (ROI) generated from software process improvement programs. It details the steps needed to compute ROI and compares these steps from the perspective of two process improvement approaches: the widely known Software Engineering Institute's capability maturity model and the approach employed by NASA's Software Engineering Laboratory (SEL). The paper then describes the specific investments made in the SEL over the past 18 years and discusses the improvements gained from this investment by the production organization in the SEL.
This paper presents the highlights and key findings of 10 years of use and study of Ada and object-oriented design in NASA Goddard's Flight Dynamics Division (FDD). In 1985, the Software Engineering Laboratory (SEL) began investigating how the Ada language might apply to FDD software development projects. Although they began cautiously using Ada on only a few pilot projects, they expected that, if the Ada pilots showed promising results, the FDD would fully transition its entire development organization from FORTRAN to Ada within 10 years. However, 10 years later, the FDD still produced 80 percent of its software in FORTRAN and had begun using C and C++, despite positive results on Ada projects. This paper presents the final results of a SEL study to quantify the impact of Ada in the FDD, to determine why Ada has not flourished, and to recommend future directions regarding Ada. Project trends in both languages are examined as are external factors and cultural issues that affected the infusion of this technology. The detailed results of this study were published in a formal study report in March of 1995. This paper supersedes the preliminary results of this study that were presented at the Eighteenth Annual Software Engineering Workshop in 1993.
Applying standard software engineering practices to neural networks is challenging due to the lack of high-level abstractions describing a neural network’s behavior. To address this challenge, we propose to extract high-level task-specific features from the neural network internal representation, based on monitoring the neural network activations.The extracted feature representations can serve as a link to high-level requirements and can be leveraged to enable fundamental software engineering activities, such as automated testing, debugging, requirements analysis, and formal verification, leading to better engineering of neural networks. Using two case studies, we present initial empirical evidence demonstrating the feasibility of our ideas.
Computer Sciences Corporation (CSC)/System Sciences Division (SSD) has maintained a long-term relationship with NASA/Goddard, providing satellite mission ground-support software and services for 23 years. As a partner in the Software Engineering Laboratory (SEL) since 1976, CSC has worked closely with NASA/Goddard to improve the software engineering process. This paper examines the evolution of reuse programs in this uniquely stable environment and formulates certain recommendations for developing reuse programs as a business strategy and as an integral part of production. It focuses on the management strategy and philosophy that have helped make reuse successful in this environment.
Knowledge-based systems have the potential to greatly increase the capabilities of future aircraft and spacecraft and to significantly reduce support manpower needed for the space station and other space missions. However, a credible validation methodology must be developed before knowledge-based systems can be used for life- or mission-critical applications. Experience with conventional software has shown that the use of good software engineering techniques and static analysis tools can greatly reduce the time needed for testing and simulation of a system. Since exhaustive testing is infeasible, reliability must be built into the software during the design and implementation phases. Unfortunately, many of the software engineering techniques and tools used for conventional software are of little use in the development of knowledge-based systems. Therefore, research at Langley is focused on developing a set of guidelines, methods, and prototype validation tools for building highly reliable, knowledge-based systems. The use of a comprehensive methodology for building highly reliable, knowledge-based systems should significantly decrease the time needed for testing and simulation. A proven record of delivering reliable systems at the beginning of the highly visible testing and simulation phases is crucial to the acceptance of knowledge-based systems in critical applications.
Practitioners of artificial intelligence are being engaged to help develop the next generation of flight software for NASA missions, in partnership with other computer scientists, mission designers, operations personnel, spacecraft engineers, systems engineers, software engineers, and scientists.
The goal of the present experiment is to characterize the fault distributions of highly reliable software replicates, constructed using techniques and environments which are similar to those used in comtemporary industrial software facilities. The fault distributions and their effect on the reliability of fault tolerant configurations of the software will be determined through extensive life testing of the replicates against carefully constructed randomly generated test data. Each detected error will be carefully analyzed to provide insight in to their nature and cause. A direct objective is to develop techniques for reducing the intensity of coincident errors, thus increasing the reliability gain which can be achieved with fault tolerance. Data on the reliability gains realized, and the cost of the fault tolerant configurations can be used to design a companion experiment to determine the cost effectiveness of the fault tolerant strategy. Finally, the data and analysis produced by this experiment will be valuable to the software engineering community as a whole because it will provide a useful insight into the nature and cause of hard to find, subtle faults which escape standard software engineering validation techniques and thus persist far into the software life cycle.