IEEE 1044-2009 en Classification for Software Anomalies (IEEE Computer Society)《软件不规则分类》.pdf
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12、#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23;#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23;#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23#23g55g48Authorized licensed use limited to: IHS Stephanie Dejesus. Downloaded on January 29, 2010 at 05:12 from IEEE
13、Xplore. Restrictions apply. Authorized licensed use limited to: IHS Stephanie Dejesus. Downloaded on January 29, 2010 at 05:12 from IEEE Xplore. Restrictions apply. IEEE Std 1044-2009 (Revision of IEEE Std 1044-1993) IEEE Standard Classification for Software Anomalies Sponsor Software +1 978 750 840
14、0. Permission to photocopy portions of any individual standard for educational classroom use can also be obtained through the Copyright Clearance Center. Authorized licensed use limited to: IHS Stephanie Dejesus. Downloaded on January 29, 2010 at 05:12 from IEEE Xplore. Restrictions apply. iv Copyri
15、ght 2010 IEEE. All rights reserved. Introduction This introduction is not part of IEEE Std 1044-2009, IEEE Standard Classification for Software Anomalies. This standard provides a uniform approach to the classification of software anomalies, regardless of when they originate or when they are encount
16、ered within the project, product, or system life cycle. Classification data can be used for a variety of purposes, including defect causal analysis, project management, and software process improvement (e.g., to reduce the likelihood of defect insertion and/or to increase the likelihood of early def
17、ect detection). Collecting the data described in this standard provides valuable information that has many useful applications. It is also well documented that the earlier within the software life cycle a problem is discovered, the cheaper, and often easier, it is to fix. This encourages the use of
18、tools, techniques, and methodologies to find problems sooner. Standard anomaly data are necessary to evaluate how well these tools, techniques, and methodologies work. These data can also identify when in a projects life cycle most problems are introduced. Distinctions between enhancements and probl
19、ems in the software help make the decisions as to which anomalies are addressed first, categories of funding, and so on. Anomaly data can also assist in the evaluation of quality attributes such as reliability and productivity. Having a standard way of classifying software anomalies is important. Fi
20、rst, it enables insight into the types of anomalies that organizations produce during development of their products. This information is a rich source of data that can be used during the execution of a project and for process improvement. Analytical techniques such as Orthogonal Defect Classificatio
21、n and causal analysis depend on classification of anomalies to identify root causes and to help determine means to prevent their recurrence. Process improvement frameworks such as Capability Maturity Model Integrated (CMMI)apromote the need for detailed understanding of process performance and produ
22、ct quality. The classification of anomalies allows the development of profiles of anomalies produced by various development processes as one indicator of process performance. Second, having a standard way to classify anomalies enables better communication and exchange of information regarding anomal
23、ies among developers and organizations. Unfortunately, people frequently associate different meanings with the same words and/or use different words to mean the same thing. Similarly, if software systems are to communicate (i.e., exchange data) effectively regarding anomalies, they must share a comm
24、on logical (if not physical) data model. Data exchange may still be possible via some mapping or translation method if the same data elements are named differently in one system as compared with another, but each system must at least recognize and implement the same conceptual objects/entities, rela
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