Common fixed points of generalized Mizoguchi-Takahashi type contractions in partial metric spaces

We give some common fixed point results for multivalued mappings in the setting of complete partial metric spaces. Our theorems extend and complement analogous results in the existing literature on metric and partial metric spaces. Finally, we provide an example to illustrate the new theory

A Comparative Analysis of Software Reliability Growth Models using defects data of Closed and Open Source Software

The purpose of this study is to compare the fitting (goodness of fit) and prediction capability of eight Software Reliability Growth Models (SRGM) using fifty different failure Data sets. These data sets contain defect data collected from system test phase, operational phase (field defects) and Open Source Software (OSS) projects. The failure data are modelled by eight SRGM (Musa Okumoto, Inflection S-Shaped, Goel Okumoto, Delayed S-Shaped, Logistic, Gompertz, Yamada Exponential, and Generalized Goel Model). These models are chosen due to their prevalence among many software reliability models. The results can be summarized as follows -Fitting capability: Musa Okumoto fits all data sets, but all models fit all the OSS datasets -Prediction capability: Musa Okumoto, Inflection S- Shaped and Goel Okumoto are the best predictors for industrial data sets, Gompertz and Yamada are the best predictors for OSS data sets - Fitting and prediction capability: Musa Okumoto and Inflection are the best performers on industrial datasets. However this happens only on slightly more than 50% of the datasets. Gompertz and Inflection are the best performers for all OSS dataset

Evidence for a Magnetic Seebeck effect

The irreversible thermodynamics of a continuous medium with magnetic dipoles predicts that a temperature gradient in the presence of magnetisation waves induces a magnetic induction field, which is the magnetic analog of the Seebeck effect. This thermal gradient modulates the precession and relaxation. The Magnetic Seebeck effect implies that magnetisation waves propagating in the direction of the temperature gradient and the external magnetic induction field are less attenuated, while magnetisation waves propagating in the opposite direction are more attenuated

Quantitative Assessment of the Impact of Automatic Static Analysis Issues on Time Efficiency

Background: Automatic Static Analysis (ASA) tools analyze source code and look for code patterns (aka smells) that might cause defective behavior or might degrade other dimensions of software quality, e.g. efficiency. There are many potentially negative code patterns, and ASA tools typically report a huge list of them even in small programs. Moreover, so far, little evidence is available about the negative impact on performance of code patterns identified by such tools. A consequence is that programmers cannot appreciate the benefits of ASA tools and tend not to include them in their workflow. Aims: Quantitatively assess the impact of issues signaled by ASA tools on time efficiency. Method: We select 20 issues and for each of them we set up two source code fragments: one containing the issue and the corresponding refactored version, functionally identical but without the issue. We set up three different platforms, isolated from network and other user programs, then we execute the code fragments, and measure the execution time of both code versions. Results: We find that eleven issues have an actual negative impact on performance. We also compute for each issue an estimation for the delay provoked by a single execution. Conclusions: We produce a set of issues with a verified negative impact on performance. They can be checked easily with an analysis tool and code can be refactored to obtain a provably more efficient code. We also provide the estimated delay cost of each issue in the environments where we conduct the tests. These results can be improved with the help of other researchers: repeating the tests in several platforms would make it possible to build up a wider benchmark

On The Impact of Passive Voice Requirements on Domain Modelling

Context: The requirements specification is a central arte- fact in the software engineering (SE) process, and its quality (might) influence downstream activities like implementation or testing. One quality defect that is often mentioned in standards is the use of passive voice. However, the con- sequences of this defect are still unclear. Goal: We need to understand whether the use of passive voice in requirements has an influence on other activities in SE. In this work we focus on domain modelling. Method: We designed an experiment, in which we ask students to draw a domain model from a given set of requirements written in active or passive voice. We compared the completeness of the resulting domain model by counting the number of missing actors, domain objects and their associations with respect to a specified solution. Results: While we could not see a difference in the number of missing actors and objects, participants which received passive sentences missed almost twice the associations. Conclusion: Our experiment indicates that, against common knowledge, actors and objects in a requirement can often be understood from the context. However, the study also shows that passive sentences complicate understanding how certain domain concepts are interconnected

A Recommender System for Telecom Users: Experimental Evaluation of Recommendation Algorithms

The increasing flourish of available services in telecom domain offers more choices to the end user. On the other hand, such wide offer cannot be completely evaluated by the user, and some services may pass unobserved even if useful. To face this issue, the usage of recommendation systems in telecom domain is growing, to directly notify the user about the presence of services which may meet user interests. Recommendation can be seen as an advanced form of personalization, because user preferences are used to predict the interests of users for a new service. In this paper we propose a recommender system for users of telecom services, based on different collaborative filtering algorithms applied to a complex data-set of telecom users. Experiments on the recommendation performance and accuracy are conducted to test the different effects of different algorithms on a data set coming from the telecom domain

Definition, implementation and validation of energy code smells: an exploratory study on an embedded system

Optimizing software in terms of energy efficiency is one of the challenges that both research and industry will have to face in the next few years.We consider energy efficiency as a software product quality characteristic, to be improved through the refactoring of appropriate code pattern: the aim of this work is identifying those code patterns, hereby defined as Energy Code Smells, that might increase the impact of software over power consumption. For our purposes, we perform an experiment consisting in the execution of several code patterns on an embedded system. These code patterns are executed in two versions: the first one contains a code issue that could negatively impact power consumption, the other one is refactored removing the issue. We measure the power consumption of the embedded device during the execution of each code pattern. We also track the execution time to investigate whether Energy Code Smells are also Performance Smells. Our results show that some Energy Code Smells actually have an impact over power consumption in the magnitude order of micro Watts. Moreover, those Smells did not introduce a performance decrease