Foundations of a Weak Measurement-Theoretic Approach to Software Measurement

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Towards a simplified definition of Function Points

3Background. COSMIC Function Points and traditional Function Points (i.e., IFPUG Function points and more recent variation of Function Points, such as NESMA and FISMA) are probably the best known and most widely used Functional Size Measurement methods. The relationship between the two kinds of Function Points still needs to be investigated. If traditional Function Points could be accurately converted into COSMIC Function Points and vice versa, then, by measuring one kind of Function Points, one would be able to obtain the other kind of Function Points, and one might measure one or the other kind interchangeably. Several studies have been performed to evaluate whether a correlation or a conversion function between the two measures exists. Specifically, it has been suggested that the relationship between traditional Function Points and COSMIC Function Points may not be linear, i.e., the value of COSMIC Function Points seems to increase more than proportionally to an increase of traditional Function Points. Objective. This paper aims at verifying this hypothesis using available datasets that collect both FP and CFP size measures. Method. Rigorous statistical analysis techniques are used, specifically Piecewise Linear Regression, whose applicability conditions are systematically checked. The Piecewise Linear Regression curve is a series of interconnected segments. In this paper, we focused on Piecewise Linear Regression curves composed of two segments. We also used Linear and Parabolic Regressions, to check if and to what extent Piecewise Linear Regression may provide an advantage over other regression techniques. We used two categories of regression techniques: Ordinary Least Squares regression is based on the usual minimization of the sum of squares of the residuals, or, equivalently, on the minimization of the average squared residual; Least Median of Squares regression is a robust regression technique that is based on the minimization of the median squared residual. Using a robust regression technique helps filter out the excessive influence of outliers. Results. It appears that the analysis of the relationship between traditional Function Points and COSMIC Function Points based on the aforementioned data analysis techniques yields valid significant models. However, different results for the various available datasets are achieved. In practice, we obtained statistically valid linear, piecewise linear, and non-linear conversion formulas for several datasets. In general, none of these is better than the others in a statistically significant manner. Conclusions. Practitioners interested in the conversion of FP measures into CFP (or vice versa) cannot just pick a conversion model and be sure that it will yield the best results. All the regression models we tested provide good results with some datasets. In practice, all the models described in the paper –in particular, both linear and non-linear ones– should be evaluated in order to identify the ones that are best suited for the specific dataset at hand.openLavazza, L.; Morasca, S.; Robiolo, G.Lavazza, LUIGI ANTONIO; Morasca, Sandro; Robiolo, G

A Dual Language Approach Extension to UML for the Development of Time-Critical Component-Based Systems

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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

An empirical evaluation of the “cognitive complexity” measure as a predictor of code understandability

Background: Code that is difficult to understand is also difficult to inspect and maintain and ultimately causes increased costs. Therefore, it would be greatly beneficial to have source code measures that are related to code understandability. Many ‘‘traditional’’ source code measures, including for instance Lines of Code and McCabe’s Cyclomatic Complexity, have been used to identify hard-to-understand code. In addition, the ‘‘Cognitive Complexity’’ measure was introduced in 2018 with the specific goal of improving the ability to evaluate code understandability. Aims: The goals of this paper are to assess whether (1) ‘‘Cognitive Complexity’’ is better correlated with code understandability than traditional measures, and (2) the availability of the ‘‘Cognitive Complexity’’ measure improves the performance (i.e., the accuracy) of code understandability prediction models. Method: We carried out an empirical study, in which we reused code understandability measures used in several previous studies. We first built Support Vector Regression models of understandability vs. code measures, and we then compared the performance of models that use ‘‘Cognitive Complexity’’ against the performance of models that do not. Results: ‘‘Cognitive Complexity’’ appears to be correlated to code understandability approximately as much as traditional measures, and the performance of models that use ‘‘Cognitive Complexity’’ is extremely close to the performance of models that use only traditional measures. Conclusions: The ‘‘Cognitive Complexity’’ measure does not appear to fulfill the promise of being a significant improvement over previously proposed measures, as far as code understandability prediction is concerned

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. This framework defines 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. 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. (Also cross-referenced as UMIACS-TR-94-119

Defining and Validating High-Level Design Metrics

The availability of significant metrics in the early phases of the software development process allows for a better management of the later phases, and a more effective quality assessment when software quality can still be easily affected by preventive or corrective actions. In this paper, we introduce and compare four strategies for defining high-level design metrics. They are based on different sets of assumptions (about the design process) related to a well defined experimental goal they help reach: identify error-prone software parts. In particular, we define ratio-scale metrics for cohesion and coupling that show interesting properties. An in-depth experimental validation, conducted on large scale projects demonstrates the usefulness of the metrics we define. (Also cross-referenced as UMIACS-TR-94-75

Towards Model-Driven Testing of a Web Application Generator

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An Investigation of the users’ perception of OSS quality

Abstract. The quality of Open Source Software (OSS) is generally much debated. Some state that it is generally higher than closed-source counterparts, while others are more skeptical. The authors have collected the opinions of the users concerning the quality of 44 OSS products in a systematic manner, so that it is now possible to present the actual opinions of real users about the quality of OSS products. Among the results reported in the paper are: the distribution of trustworthiness of OSS based on our survey; a comparison of the trustworthiness of the surveyed products with respect to both open and closed-source competitors; the identification of the qualities that affect the perception of trustworthiness, based on rigorous statistical analysis