Software quality metrics methodology _tanmi kiran
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This document outlines the IEEE standard for a software quality metrics methodology. It describes the purpose of establishing software quality metrics which is to quantify quality factors and ensure quality requirements are met. It provides steps for developing a software quality metrics program which include determining quality requirements, selecting metrics, collecting and storing data, validating metrics, and revalidating metrics when the environment changes. The goal is to use metrics to predict and measure software quality in order to identify issues and ensure compliance with quality standards.
Software quality metrics methodology _tanmi kiran
- 1. Kiran Lata Gangwar Tanmi Kapoor M.Tech (S.E.)
- 2. IEEE Standard for a Software Quality Metrics Methodology Sponsor Software Engineering Standards Committee of the IEEE Computer Society Approved 8 December 1998 IEEE-SA Standards Board
- 5. Use, organizational experience required standards regulations laws Consider, contractual requirements cost or schedule constraints warranties customer metrics requirements organizational self-interest
- 6. 1.2 Determine the list of quality requirements: Survey all involved parties Create the list of quality requirements 1.3 Quantify each quality factor: For each quality factor, assign one or more direct metrics to represent the quality factor assign direct metric values to serve as quantitative requirements for that quality factor
- 9. Identify tools Describe data storage procedures Establish a traceability matrix Identify the organizational entities Participate in data collection Responsible for monitoring data collection Describe the training and experience required for data collection Training process for personnel involved
- 11. Test the data collection and metrics computation procedures on selected software that will act as a prototype Select samples that are similar to the project(s) on which the metrics will be used Examine the cost of the measurement process for the prototype to verify or improve the cost analysis Results collected from the prototype to improve the metric descriptions and descriptions of data items
- 12. Using the formats in Table, collect and store data in the project metrics database at the appropriate time in the life cycle Check the data for accuracy and proper unit of measure Monitor the data collection Check for uniformity of data if more than one person is collecting it Compute the metric values from the collected data
- 14. Interpret and record the results Analyze the differences between the collected metric data and the target values Investigate significant differences
- 15. Interpret and record the results Unacceptable quality may be manifested as excessive complexity, inadequate documentation, lack of traceability, or other undesirable attributes
- 16. Use validated metrics during development to make predictions of direct metric values Make predictions for software components and process steps Analyze in detail software components and process steps whose predicted direct metric values deviate from the target values
- 17. Use direct metrics to ensure compliance of software products with quality requirements during system and acceptance testing Use direct metrics for software components and process steps. Compare these metric values with target values of the direct metrics Classify software components and process steps whose measurements deviate from the target values as noncompliant
- 19. The purpose of metrics validation is to identify both product and process metrics that can predict specified quality factor values, which are quantitative representations of quality requirements Metrics shall indicate whether quality requirements have been achieved or are likely to be achieved in the future
- 20. For the purpose of assessing whether a metric is valid The following thresholds shall be designated: V-square of the linear correlation coefficient B-rank correlation coefficient A-prediction error @-confidence level P-success rate
- 21. a) Correlation b) Tracking c) Consistency d) Predictability e) Discriminative power f) Reliability
- 22. 5.3.1 Identify the quality factors sample A sample of quality factors shall be drawn from the metrics database 5.3.2 Identify the metrics sample A sample from the same domain (e.g., same software components), as used in 5.3.1, shall be drawn from the metrics database
- 23. 5.3.3 Perform a statistical analysis The analysis described in 5.2 shall be performed Before a metric is used to evaluate the quality of a product or process, it shall be validated against the criteria described in 5.2. If a metric does not pass all of the validity tests, it shall only be used according to the criteria prescribed by those tests 5.3.4 Document the results Documented results shall include the direct metric, predictive metric, validation criteria, and numerical results, as a minimum
- 24. 5.3.5 Revalidate the metrics A validated metric may not necessarily be valid in other environments or future applications. Therefore, a predictive metric shall be revalidated before it is used for another environment or application 5.3.6 Evaluate the stability of the environment Metrics validation shall be undertaken in a stable development environment (i.e., where the design language, implementation language, or project development tools do not change over the life of the project in which validation is performed)