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At least 73 records · Page 4

Software unit testing in Ada environment

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.

Warnock, Glenn↗

Reuse Metrics for Object Oriented Software

One way to increase the quality of software products and the productivity of software development is to reuse existing software components when building new software systems. In order to monitor improvements in reuse, the level of reuse must be measured. In this NASA supported project we (1) derived a suite of metrics which quantify reuse attributes for object oriented, object based, and procedural software, (2) designed prototype tools to take these measurements in Ada, C++, Java, and C software, (3) evaluated the reuse in available software, (4) analyzed the relationship between coupling, cohesion, inheritance, and reuse, (5) collected object oriented software systems for our empirical analyses, and (6) developed quantitative criteria and methods for restructuring software to improve reusability.

Bieman, James M.↗

Configuration management and software measurement in the Ground Systems Development Environment (GSDE)

A set of functional requirements for software configuration management (CM) and metrics reporting for Space Station Freedom ground systems software are described. This report is one of a series from a study of the interfaces among the Ground Systems Development Environment (GSDE), the development systems for the Space Station Training Facility (SSTF) and the Space Station Control Center (SSCC), and the target systems for SSCC and SSTF. The focus is on the CM of the software following delivery to NASA and on the software metrics that relate to the quality and maintainability of the delivered software. The CM and metrics requirements address specific problems that occur in large-scale software development. Mechanisms to assist in the continuing improvement of mission operations software development are described.

Church, Victor E.↗

Understanding software faults and their role in software reliability modeling

This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.

Munson, John C.↗

The Role and Quality of Software Safety in the NASA Constellation Program

In this study, we examine software safety risk in the early design phase of the NASA Constellation spaceflight program. Obtaining an accurate, program-wide picture of software safety risk is difficult across multiple, independently-developing systems. We leverage one source of safety information, hazard analysis, to provide NASA quality assurance managers with information regarding the ongoing state of software safety across the program. The goal of this research is two-fold: 1) to quantify the relative importance of software with respect to system safety; and 2) to quantify the level of risk presented by software in the hazard analysis. We examined 154 hazard reports created during the preliminary design phase of three major flight hardware systems within the Constellation program. To quantify the importance of software, we collected metrics based on the number of software-related causes and controls of hazardous conditions. To quantify the level of risk presented by software, we created a metric scheme to measure the specificity of these software causes. We found that from 49-70% of hazardous conditions in the three systems could be caused by software or software was involved in the prevention of the hazardous condition. We also found that 12-17% of the 2013 hazard causes involved software, and that 23-29% of all causes had a software control. Furthermore, 10-12% of all controls were software-based. There is potential for inaccuracy in these counts, however, as software causes are not consistently scoped, and the presence of software in a cause or control is not always clear. The application of our software specificity metrics also identified risks in the hazard reporting process. In particular, we found a number of traceability risks in the hazard reports may impede verification of software and system safety.

Layman, Lucas↗

Production of Reliable Flight Crucial Software: Validation Methods Research for Fault Tolerant Avionics and Control Systems Sub-Working Group Meeting

The state of the art in the production of crucial software for flight control applications was addressed. The association between reliability metrics and software is considered. Thirteen software development projects are discussed. A short term need for research in the areas of tool development and software fault tolerance was indicated. For the long term, research in format verification or proof methods was recommended. Formal specification and software reliability modeling, were recommended as topics for both short and long term research.

Dunham, J. R.↗

Metrics

A development status evaluation is presented for practical software performance measurement, or 'metrics', in which major innovations have recently occurred. Metrics address such aspects of software performance as whether a software project is on schedule, how many errors can be expected from it, whether the methodology being used is effective and the relative quality of the software employed. Metrics may be characterized as explicit, analytical, and subjective. Attention is given to the bases for standards and the conduct of metrics research.

Mcgarry, Frank↗

A Predictive Approach to Eliminating Errors in Software Code

NASA s Metrics Data Program Data Repository is a database that stores problem, product, and metrics data. The primary goal of this data repository is to provide project data to the software community. In doing so, the Metrics Data Program collects artifacts from a large NASA dataset, generates metrics on the artifacts, and then generates reports that are made available to the public at no cost. The data that are made available to general users have been sanitized and authorized for publication through the Metrics Data Program Web site by officials representing the projects from which the data originated. The data repository is operated by NASA s Independent Verification and Validation (IV&V) Facility, which is located in Fairmont, West Virginia, a high-tech hub for emerging innovation in the Mountain State. The IV&V Facility was founded in 1993, under the NASA Office of Safety and Mission Assurance, as a direct result of recommendations made by the National Research Council and the Report of the Presidential Commission on the Space Shuttle Challenger Accident. Today, under the direction of Goddard Space Flight Center, the IV&V Facility continues its mission to provide the highest achievable levels of safety and cost-effectiveness for mission-critical software. By extending its data to public users, the facility has helped improve the safety, reliability, and quality of complex software systems throughout private industry and other government agencies. Integrated Software Metrics, Inc., is one of the organizations that has benefited from studying the metrics data. As a result, the company has evolved into a leading developer of innovative software-error prediction tools that help organizations deliver better software, on time and on budget.

Source record↗

Integrating automated support for a software management cycle into the TAME system

Software managers are interested in the quantitative management of software quality, cost and progress. An integrated software management methodology, which can be applied throughout the software life cycle for any number purposes, is required. The TAME (Tailoring A Measurement Environment) methodology is based on the improvement paradigm and the goal/question/metric (GQM) paradigm. This methodology helps generate a software engineering process and measurement environment based on the project characteristics. The SQMAR (software quality measurement and assurance technology) is a software quality metric system and methodology applied to the development processes. It is based on the feed forward control principle. Quality target setting is carried out before the plan-do-check-action activities are performed. These methodologies are integrated to realize goal oriented measurement, process control and visual management. A metric setting procedure based on the GQM paradigm, a management system called the software management cycle (SMC), and its application to a case study based on NASA/SEL data are discussed. The expected effects of SMC are quality improvement, managerial cost reduction, accumulation and reuse of experience, and a highly visual management reporting system.

Sunazuka, Toshihiko↗

Towards Streamlining Auditing for Compliance With Requirements in Open-Source Software at NASA

Context: NASA requires all software to meet several requirements (NPR 7150.2) depending on software criticality. The instantiation of these requirements may vary per project; however, once decided upon, projects must undergo audits to evaluate compliance with these requirements. Aim: We propose that audit effort can be reduced when requirements are realized by leveraging commonly used open-source infrastructure for version control, issue tracking and continuous integration, and the generated records are analyzed using a repository mining software tool to quantify process compliance. Method: We perform a case study in the NASA-funded Copilot project, utilizing Kaiaulu, a repository mining software tool. We define four software compliance metrics based on the Copilot’s requirements, and analyze their impact on source code quality. Results: Our work demonstrates how it is possible to leverage existing open source tools and platforms to facilitate software certification and qualification, and to streamline the auditing process required even when stringent requirements must be enforced. Conclusion: Together, both project and tool can be utilized to visualize project compliance, and metrics can be defined to more easily identify process irregularities to minimize auditing efforts. Project Repository: github.com/Copilot-Language/copilot Tool Repository: github.com/sailuh/kaiaulu

code-quality↗

The role of metrics and measurements in a software intensive total quality management environment

Paramax Space Systems began its mission as a member of the Rockwell Space Operations Company (RSOC) team which was the successful bidder on a massive operations consolidation contract for the Mission Operations Directorate (MOD) at JSC. The contract awarded to the team was the Space Transportation System Operations Contract (STSOC). Our initial challenge was to accept responsibility for a very large, highly complex and fragmented collection of software from eleven different contractors and transform it into a coherent, operational baseline. Concurrently, we had to integrate a diverse group of people from eleven different companies into a single, cohesive team. Paramax executives recognized the absolute necessity to develop a business culture based on the concept of employee involvement to execute and improve the complex process of our new environment. Our executives clearly understood that management needed to set the example and lead the way to quality improvement. The total quality management policy and the metrics used in this endeavor are presented.

Daniels, Charles B.↗

Full Life-Cycle Defect Management Assessment: Initial Inspection Data Collection Results and Research Questions for Further Study

It is often the case in software projects that when schedule and budget resources are limited, the Verification and Validation (V&V) activities suffer. Fewer V&V activities can be afforded and moreover, short-term challenges can result in V&V activities being scaled back or dropped altogether. As a result, too often the default solution is to save activities for improving software quality until too late in the life-cycle, relying on late-term code inspections followed by thorough testing activities to reduce defect counts to acceptable levels. As many project managers realize, however, this is a resource-intensive way of achieving the required quality for software. The Full Life-cycle Defect Management Assessment Initiative, funded by NASA s Office of Safety and Mission Assurance under the Software Assurance Research Program, aims to address these problems by: Improving the effectiveness of early life-cycle V&V activities to make their benefits more attractive to team leads. Specifically, we focus on software inspection, a proven method that can be applied to any software work product, long before executable code has been developed; Better communicating this effectiveness to software development teams, along with suggestions for parameters to improve in the future to increase effectiveness; Analyzing the impact of early life-cycle V&V on the effectiveness and cost required for late life-cycle V&V activities, such as testing, in order to make the tradeoffs more apparent. This white paper reports on an initial milestone in this work, the development of a preliminary model of inspection effectiveness across multiple NASA Centers. This model contributes toward reaching our project goals by: Allowing an examination of inspection parameters, across different types of projects and different work products, for an analysis of factors that impact defect detection effectiveness. Allowing a comparison of this NASA-specific model to existing recommendations in the literature regarding how to plan effective inspections. Forming a baseline model which can be extended to incorporate factors describing: the numbers and types of defects that are missed by inspections; how such defects flow downstream through software development phases; how effectively they can be caught by testing activities in the late stages of development. The model has been implemented in a prototype web-enabled decision-support tool which allows developers to enter their inspection data and receive feedback based on a comparison against the model. The tool also allows users to access reusable materials (such as checklists) from projects included in the baseline. Both the tool itself and the model underlying it will continue to be extended throughout the remainder of this initiative. As results of analyzing inspection effectiveness for defect containment are determined, they can be shared via the tool and also via updates to existing training courses on metrics and software inspections. Moreover, the tool will help satisfy key CMMI requirements for the NASA Centers, as it will enable NASA to take a global view across peer review results for various types of projects to identify systemic problems. This analysis can result in continuous improvements to the approach to verification.

Shull, Forrest↗

Quantitative software models for the estimation of cost, size, and defects

The presentation will provide a brief overview of the SQI measurement program as well as describe each of these models and how they are currently being used in supporting JPL project, task and software managers to estimate and plan future software systems and subsystems.

software metrics process improvement↗