Iso Iec Ieee 15939

[Theodor Urena]

ISOIEC 15939:2007 defines a measurement process applicable to system and software engineering and management disciplines. The process is described through a model that defines the activities of the measurement process that are required to adequately specify what measurement information is required, how the measures and analysis results are to be applied, and how to determine if the analysis results are valid. The measurement process is flexible, tailorable, and adaptable to the needs of different users.

ISO/IEC 15939:2007 identifies a process that supports defining a suitable set of measures that address specific information needs. It identifies the activities and tasks that are necessary to successfully identify, define, select, apply and improve measurement within an overall project or organizational measurement structure. It also provides definitions for measurement terms commonly used within the system and software industries.

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ISO/IEC/IEEE 15939:2017 provides an elaboration of the measurement process from ISO/IEC 15288 and ISO/IEC 12207. The measurement process is applicable to system and software engineering and management disciplines. The process is described through a model that defines the activities of the measurement process that are required to adequately specify what measurement information is required, how the measures and analysis results are to be applied, and how to determine if the analysis results are valid. The measurement process is flexible, tailorable, and adaptable to the needs of different users.

ISO/IEC/IEEE 15939:2017 identifies a process that supports defining a suitable set of measures that address specific information needs. It identifies the activities and tasks that are necessary to successfully identify, define, select, apply, and improve measurement within an overall project or organizational measurement structure. It also provides definitions for commonly used measurement terms.

Measurement and the accompanying analysis are fundamental elements of systems engineering (SE) and technical management. SE measurement provides information relating to the products developed, services provided, and processes implemented to support effective management of the processes and to objectively evaluate product or service quality. Measurement supports realistic planning, provides insight into actual performance, and facilitates assessment of suitable actions (Roedler and Jones 2005, 1-65; Frenz et al. 2010).

Appropriate measures and indicators are essential inputs to tradeoff analyses to balance cost, schedule, and technical objectives. Periodic analysis of the relationships between measurement results and review of the requirements and attributes of the system provides insights that help to identify issues early, when they can be resolved with less impact. Historical data, together with project or organizational context information, forms the basis for the predictive models and methods that should be used.

This approach has been the basis for establishing a common process across the software and systems engineering communities. This measurement approach has been adopted by the Capability Maturity Model Integration (CMMI) measurement and analysis process area (SEI 2006, 10), as well as by international systems and software engineering standards (ISO/IEC/IEEE 15939; ISO/IEC/IEEE 15288, 1). The International Council on Systems Engineering (INCOSE) Measurement Working Group has also adopted this measurement approach for several of their measurement assets, such as the INCOSE SE Measurement Primer (Frenz et al. 2010) and Technical Measurement Guide (Roedler and Jones 2005). This approach has provided a consistent treatment of measurement that allows the engineering community to communicate more effectively about measurement. The process is illustrated in Figure 1 from Roedler and Jones (2005) and McGarry et al. (2002).

This activity focuses on establishing the resources, training, and tools to implement a measurement process and ensure that there is a management commitment to use the information that is produced. Refer to PSM (August 18, 2011) and SPC (2011) for additional detail.

This activity focuses on defining measures that provide insight into project or organization information needs. This includes identifying what the decision-makers need to know and when they need to know it, relaying these information needs to those entities in a manner that can be measured, and identifying, prioritizing, selecting, and specifying measures based on project and organization processes (Jones 2003, 15-19). This activity also identifies the reporting format, forums, and target audience for the information provided by the measures.

The INCOSE SE Measurement Primer (Frenz et al. 2010) provides a list of attributes of a good measure with definitions for each attribute; these attributes include relevance, completeness, timeliness, simplicity, cost effectiveness, repeatability, and accuracy. Evaluating candidate measures against these attributes can help assure the selection of more effective measures.

The details of each measure need to be unambiguously defined and documented. Templates for the specification of measures and indicators are available on the PSM website (2011) and in Goethert and Siviy (2004).

This activity focuses on the collection and preparation of measurement data, measurement analysis, and the presentation of the results to inform decision makers. The preparation of the measurement data includes verification, normalization, and aggregation of the data, as applicable. Analysis includes estimation, feasibility analysis of plans, and performance analysis of actual data against plans.

The quality of the measurement results is dependent on the collection and preparation of valid, accurate, and unbiased data. Data verification, validation, preparation, and analysis techniques are discussed in PSM (2011) and SEI (2010). Per TL 9000, Quality Management System Guidance, The analysis step should integrate quantitative measurement results and other qualitative project information, in order to provide managers the feedback needed for effective decision making (QuEST Forum 2012, 5-10). This provides richer information that gives the users the broader picture and puts the information in the appropriate context.

There is a significant body of guidance available on good ways to present quantitative information. Edward Tufte has several books focused on the visualization of information, including The Visual Display of Quantitative Information (Tufte 2001).

This activity involves the analysis of information that explains the periodic evaluation and improvement of the measurement process and specific measures. One objective is to ensure that the measures continue to align with the business goals and information needs, as well as provide useful insight. This activity should also evaluate the SE measurement activities, resources, and infrastructure to make sure it supports the needs of the project and organization. Refer to PSM (2011) and Practical Software Measurement: Objective Information for Decision Makers (McGarry et al. 2002) for additional detail.

Leading indicators are aimed at providing predictive insight that pertains to an information need. A SE leading indicator is a measure for evaluating the effectiveness of a how a specific activity is applied on a project in a manner that provides information about impacts that are likely to affect the system performance objectives (Roedler et al. 2010). Leading indicators may be individual measures or collections of measures and associated analysis that provide future systems engineering performance insight throughout the life cycle of the system; they support the effective management of systems engineering by providing visibility into expected project performance and potential future states (Roedler et al. 2010).

As shown in Figure 2, a leading indicator is composed of characteristics, a condition, and a predicted behavior. The characteristics and conditions are analyzed on a periodic or as-needed basis to predict behavior within a given confidence level and within an accepted time range into the future. More information is also provided by Roedler et al. (2010).

Technical measurement is the set of measurement activities used to provide information about progress in the definition and development of the technical solution, ongoing assessment of the associated risks and issues, and the likelihood of meeting the critical objectives of the acquirer. This insight helps an engineer make better decisions throughout the life cycle of a system and increase the probability of delivering a technical solution that meets both the specified requirements and the mission needs. The insight is also used in trade-off decisions when performance is not within the thresholds or goals.

Technical measurement includes measures of effectiveness (MOEs), measures of performance (MOPs), and technical performance measures (TPMs) (Roedler and Jones 2005, 1-65). The relationships between these types of technical measures are shown in Figure 3 and explained in the reference for Figure 3. Using the measurement process described above, technical measurement can be planned early in the life cycle and then performed throughout the life cycle with increasing levels of fidelity as the technical solution is developed, facilitating predictive insight and preventive or corrective actions. More information about technical measurement can be found in the NASA Systems Engineering Handbook, System Analysis, Design, Development: Concepts, Principles, and Practices, and the Systems Engineering Leading Indicators Guide (NASA December 2007, 1-360, Section 6.7.2.2; Wasson 2006, Chapter 34; Roedler and Jones 2005).

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