减少软件缺陷前10名排行榜

软件本身的复杂性及其开发进程的不断加速使得软件缺陷难以避免，然而，以下10种技巧可帮你减少编程中出现缺陷。     

Validation of an approach for improving existing measurementframeworks

Software organizations are in need of methods to understand, structure, and improve the data their are collecting. We have developed an approach for use when a large number of diverse metrics are already being collected by a software organization (M.G. Mendonca et al., 1998; M.G. Mendonca, 1997). The approach combines two methods. One looks at an organization's measurement framework in a top-down goal-oriented fashion and the other looks at it in a bottom-up data-driven fashion. The top-down method is based on a measurement paradigm called Goal-Question-Metric (GQM). The bottom-up method is based on a data mining technique called Attribute Focusing (AF). A case study was executed to validate this approach and to assess its usefulness in an industrial environment. The top-down and bottom-up methods were applied in the customer satisfaction measurement framework at the IBM Toronto Laboratory. The top-down method was applied to improve the customer satisfaction (CUSTSAT) measurement from the point of view of three data user groups. It identified several new metrics for the interviewed groups, and also contributed to better understanding of the data user needs. The bottom-up method was used to gain new insights into the existing CUSTSAT data. Unexpected associations between key variables prompted new business insights, and revealed problems with the process used to collect and analyze the CUSTSAT data. The paper uses the case study and its results to qualitatively compare our approach against current ad hoc practices used to improve existing measurement frameworks     

Special Feature: The Flex Software Design System: Designers Need Languages, too

An experimental design language gives evidence that software design may benefit from the same formal techniques programmers are using.     

An evaluation of expert systems for software engineering management

The development of four separate, prototype expert systems to aid in software engineering management is described. Given the values for certain metrics, these systems provide interpretations which explain any abnormal patterns of these values during the development of a software project. The four expert systems which solve the same problem, were built using two different approaches to knowledge acquisition, a bottom-up approach and a top-down approach and two different expert system methods, rule-based deduction and frame-based abduction. In a comparison to see which methods might better suit the needs of this field, it was found that the bottom-up approach led to better results that did the top-down approach, and the rule-based deduction systems using simple rules provided more complete and correct solutions than did the frame-based abduction systems     

Supervised semantic relation mining from linguistically noisy text documents

In this paper, we present models for mining text relations between named entities, which can deal with data highly affected by linguistic noise. Our models are made robust by: (a) the exploitation of state-of-the-art statistical algorithms such as support vector machines (SVMs) along with effective and versatile pattern mining methods, e.g. word sequence kernels; (b) the design of specific features capable of capturing long distance relationships; and (c) the use of domain prior knowledge in the form of ontological constraints, e.g. bounds on the type of relation arguments given by the semantic categories of the involved entities. This property allows for keeping small the training data required by SVMs and consequently lowering the system design costs. We empirically tested our hybrid model in the very complex domain of business intelligence, where the textual data are constituted by reports on investigations into criminal enterprises based on police interrogatory reports, electronic eavesdropping and wiretaps. The target relations are typically established between entities, as they are mentioned in these information sources. The experiments on mining such relations show that our approach with small training data is robust to non-conventional languages as dialects, jargon expressions or coded words typically contained in such text.     

How perspective-based reading can improve requirements inspections

Because defects constitute an unavoidable aspect of software development, discovering and removing them early is crucial. Overlooked defects (like faults in the software system requirements, design, or code) propagate to subsequent development phases where detecting and correcting them becomes more difficult. At best, developers will eventually catch the defects, but at the expense of schedule delays and additional product-development costs. At worst, the defects will remain, and customers will receive a faulty product. The authors explain their perspective based reading (PBR) technique that provides a set of procedures to help developers solve software requirements inspection problems. PBR reviewers stand in for specific stakeholders in the document to verify the quality of requirements specifications. The authors show how PBR leads to improved defect detection rates for both individual reviewers and review teams working with unfamiliar application domains