Engineering topics

Morasca, Sandro

Publications and source records attributed to Morasca, Sandro.

4 records

NASA NTRS1997

Property-Based Software Engineering Measurement

Little theory exists in the field of software system measurement. Concepts such as complexity, coupling, cohesion or even size are very often subject to interpretation and appear to have inconsistent definitions in the literature. As a consequence, there is little guidance provided to the analyst attempting to define proper measures for specific problems. Many controversies in the literature are simply misunderstandings and stem from the fact that some people talk about different measurement concepts under the same label (complexity is the most common case). There is a need to define unambiguously the most important measurement concepts used in the measurement of software products. One way of doing so is to define precisely what mathematical properties characterize these concepts, regardless of the specific software artifacts to which these concepts are applied. Such a mathematical framework could generate a consensus in the software engineering community and provide a means for better communication among researchers, better guidelines for analysts, and better evaluation methods for commercial static analyzers for practitioners. In this paper, we propose a mathematical framework which is generic, because it is not specific to any particular software artifact and rigorous, because it is based on precise mathematical concepts. We use this framework to propose definitions of several important measurement concepts (size, length, complexity, cohesion, coupling). It does not intend to be complete or fully objective; other frameworks could have been proposed and different choices could have been made. However, we believe that the formalisms and properties we introduce are convenient and intuitive. This framework contributes constructively to a firmer theoretical ground of software measurement.

Briand, Lionel C.↗

NASA NTRS1995

Goal-Driven Definition of Product Metrics Based on Properties

Defining product metrics requires a rigorous and disciplined approach, because useful metrics depend, to a very large extent, on one's goals and assumptions about the studied software process. Unlike in more mature scientific fields, it appears difficult to devise a "universal" set of metrics in software engineering, that can be used across application environments. We propose an approach for the definition of product metrics which is driven by the experimental goals of measurement, expressed via the Goal/Question/Metric (GQM) paradigm, and is based on the mathematical properties of the metrics. This approach integrates several research contributions from the literature into a consistent, practical and rigorous approach. The approach we outline should not be considered as a complete and definitive solution, but as a starting point for discussion about a product metric definition approach widely accepted in the software engineering community. At this point, we intend to provide an intellectual process that we think is necessary to define sound software product metrics. A precise and complete documentation of such an approach will provide the information needed to make the assessment and reuse of a new metric possible. Thus, product metrics are supported by a solid theory which facilitates their review and refinement. Moreover, their definition is made less exploratory and, as a consequence, one is less likely to identify spurious correlations between process and product metrics.

Briand, Lionel↗

NASA NTRS1995

Property-Based Software Engineering Measurement

Little theory exists in the field of software system measurement. Concepts such as complexity, coupling, cohesion or even size are very often subject to interpretation and appear to have inconsistent definitions in the literature. As a consequence, there is little guidance provided to the analyst attempting to define proper measures for specific problems. Many controversies in the literature are simply misunderstandings and stem from the fact that some people talk about different measurement concepts under the same label (complexity is the most common case). There is a need to define unambiguously the most important measurement concepts used in the measurement of software products. One way of doing so is to define precisely what mathematical properties characterize these concepts regardless of the specific software artifacts to which these concepts are applied. Such a mathematical framework could generate a consensus in the software engineering community and provide a means for better communication among researchers, better guidelines for analysis, and better evaluation methods for commercial static analyzers for practitioners. In this paper, we propose a mathematical framework which is generic, because it is not specific to any particular software artifact, and rigorous, because it is based on precise mathematical concepts. This framework defines several important measurement concepts (size, length, complexity, cohesion, coupling). It is not intended to be complete or fully objective; other frameworks could have been proposed and different choices could have been made. However, we believe that the formalism and properties we introduce are convenient and intuitive. In addition, we have reviewed the literature on this subject and compared it with our work. This framework contributes constructively to a firmer theoretical ground of software measurement.

Briand, Lionel↗

NASA NTRS1993

Measuring and assessing maintainability at the end of high level design

Software architecture appears to be one of the main factors affecting software maintainability. Therefore, in order to be able to predict and assess maintainability early in the development process we need to be able to measure the high-level design characteristics that affect the change process. To this end, we propose a measurement approach, which is based on precise assumptions derived from the change process, which is based on Object-Oriented Design principles and is partially language independent. We define metrics for cohesion, coupling, and visibility in order to capture the difficulty of isolating, understanding, designing and validating changes.

Briand, Lionel C.↗