How changes affect software entropy: an empirical study

Context Software systems continuously change for various reasons, such as adding new features, fixing bugs, or refactoring. Changes may either increase the source code complexity and disorganization, or help to reducing it. Aim This paper empirically investigates the relationship of source code complexity and disorganization—measured using source code change entropy—with four factors, namely the presence of refactoring activities, the number of developers working on a source code file, the participation of classes in design patterns, and the different kinds of changes occurring on the system, classified in terms of their topics extracted from commit notes. Method We carried out an exploratory study on an interval of the life-time span of four open source systems, namely ArgoUML, Eclipse-JDT, Mozilla, and Samba, with the aim of analyzing the relationship between the source code change entropy and four factors: refactoring activities, number of contributors for a file, participation of classes in design patterns, and change topics. Results The study shows that (i) the change entropy decreases after refactoring, (ii) files changed by a higher number of developers tend to exhibit a higher change entropy than others, (iii) classes participating in certain design patterns exhibit a higher change entropy than others, and (iv) changes related to different topics exhibit different change entropy, for example bug fixings exhibit a limited change entropy while changes introducing new features exhibit a high change entropy. Conclusions Results provided in this paper indicate that the nature of changes (in particular changes related to refactorings), the software design, and the number of active developers are factors related to change entropy. Our findings contribute to understand the software aging phenomenon and are preliminary to identifying better ways to contrast it.     

Relevance of functional foods in the Mediterranean diet: the role of olive oil,berries and honey in the prevention of cancer and cardiovascular diseases

Abstract

The traditional Mediterranean diet (MedDiet) is a well-known dietary pattern associated with longevity and improvement of life quality as it reduces the risk of the most common chronic pathologies, such as cancer and cardiovascular diseases (CVDs), that represent the principal cause of death worldwide. One of the most characteristic foods of MedDiet is olive oil, a very complex matrix, which constitutes the main source of fats and is used in the preparation of foods, both raw as an ingredient in recipes, and in cooking. Similarly, strawberries and raspberries are tasty and powerful foods which are commonly consumed in the Mediterranean area in fresh and processed forms and have attracted the scientific and consumer attention worldwide for their beneficial properties for human health. Besides olive oil and berries, honey has lately been introduced in the MedDiet thanks to its relevant nutritional, phytochemical and antioxidant profile. It is a sweet substance that has recently been classified as a functional food. The aim of this review is to present and discuss the recent evidence, obtained from in vitro, in vivo and epidemiological studies, on the potential roles exerted by these foods in the prevention and progression of different types of cancer and CVDs.     

Mode III crack growth in linear hardening materials with strain gradient effects

The flow-theory version of couple stress strain gradient plasticity is adopted for investigating the asymptotic fields near a steadily propagating crack-tip, under Mode III loading conditions. By incorporating a material characteristic length, typically of the order of few microns for ductile metals, the adopted constitutive model accounts for the microstructure of the material and can capture the strong size effects arising at small scales. The effects of microstructure result in a substantial increase in the singularities of the skew-symmetric stress and couple stress fields, which occurs also for a small hardening coefficient. The symmetric stress field turns out to be non-singular according to the asymptotic solution for the stationary crack problem in linear elastic couple stress materials. The performed asymptotic analysis can provide useful predictions about the increase of the traction level ahead of the crack-tip due to the sole contribution of the rotation gradient, which has been found relevant and non-negligible at the micron scale.     

Sampling Errors in the Estimation of Peak Wind-Induced Internal Forces in Low-Rise Structures

Peak values in time histories of wind effects may be obtained by using the entire information inherent in the time series of the wind effect, a method entailing the estimation of the probability distribution of the peak through the application of the classical Rice procedure extended for non-Gaussian time histories. We present estimates of the sampling errors inherent in this method. These are needed for structural reliability calculations and for decisions on the requisite length of wind tunnel pressure records, especially for database-assisted design. If based on the analysis of 1-h-long records generated by Monte Carlo simulation, typical sampling errors in the estimation of peaks of time histories corresponding to windstorms of 1-h duration are about 5%. If based on 30- or 20-min records, they are about 1.5 times or twice as large, respectively. Consideration of the sampling errors in reliability calculations entails an estimated increase in the requisite safety margins with respect to wind loading of roughly 2, 3, and 5% if 1-h, 30-min, or 20-min records are used, respectively.     

Probabilistic Design space determination in pharmaceutical product development: A Bayesian/latent variable approach

To find the design space (DS) of a pharmaceutical process, quantification of the “assurance of quality” for the product under development is required. In this study, latent‐variable modeling is combined with multivariate Bayesian regression to identify a subset of input combinations (process operating conditions and raw materials properties) within which the DS of the product will lie at a probability equal to, or greater than, an assigned threshold. Partial least‐squares regression is used to obtain a linear transformation between the original multidimensional input space and a low‐dimensional latent space. The input domain is then discretized on its lower dimensional representation and a Bayesian posterior predictive approach is used to quantify the probability that the critical quality attributes of the product will meet their specifications for each discretization point. The methodology is tested on two case studies taken from the literature, one of which involving experimental data. The ability of the proposed approach to obtain a probabilistic identification of the DS, while simultaneously reducing the computational burden for the discretization of the input domain and providing a simple graphical representation of the DS, is shown. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2438–2449, 2018     

Enhanced Visualization of Detected 3D Geometric Differences

The wide availability of 3D acquisition devices makes viable their use for shape monitoring. The current techniques for the analysis of time‐varying data can efficiently detect actual significant geometric changes and rule out differences due to irrelevant variations (such as sampling, lighting and coverage). On the other hand, the effective visualization of such detected changes can be challenging when we want to show at the same time the original appearance of the 3D model. In this paper, we propose a dynamic technique for the effective visualization of detected differences between two 3D scenes. The presented approach, while retaining the original appearance, allows the user to switch between the two models in a way that enhances the geometric differences that have been detected as significant. Additionally, the same technique is able to visually hides the other negligible, yet visible, variations. The main idea is to use two distinct screen space time‐based interpolation functions for the significant 3D differences and for the small variations to hide. We have validated the proposed approach in a user study on a different class of datasets, proving the objective and subjective effectiveness of the method.     

Sources of information for innovation: the role of companies’ motivations

The paper analyses the information sources that companies use to innovate. The analysis compares two science-oriented sources of information (universities and public research institutes) with two market-oriented sources (clients and suppliers). The analysis demonstrates the importance of motivations as determinants of the sources of information used. Adding motivations to the established ‘structural’ explanations provides a new way of capturing companies’ search for information and emphasises the direction rather than the breadth of the search process. The results of this analysis also have implications both for policy-making and for companies’ managers, which are discussed in the paper’s conclusions.