Software Development Effort Estimation Using Regression Fuzzy Models

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call regression fuzzy logic. State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.Comment: This paper has been accepted in January 2019 in Computational Intelligence and Neuroscience Journal (In Press

Cooperative learning of requirements engineering through an international educational scenario enabled by the MOY programme

The International Excellence Campus for Higher Education and Research of the Region of Murcia, and the Mediterranean Office for Youth (MOY) programme are new initiatives that offer opportunities for designing educational activities in which can take part international students enrolled in academic degrees at different universities. Besides, a significant rise in distributed and collaborative software development has been observed in recent years (Global Software Development, GSD), which involves space, time and socio-cultural distances and requires new techniques, tools and practices to meet new challenges and opportunities. In addition, poor requirements are one of the most common causes of project failure in any domain. Projects which devote more resources to Requirements Engineering (RE) result in lower costs and lower deviations of their planning. Therefore, the relevance of education and training the future systems and software professionals in RE activities and techniques, in particular in GSD environments, must be stressed. We have conducted an educational innovation activity based on teaching RE in co-located and GSD contexts. This activity has been carried out in the form of an experiment with students. This paper presents the scenario in which this educational activity is framed as well as some preliminary results of this experiment

Dosimetric comparison between coplanar and non coplanar field radiotherapy for ethmoid sinus cancer

<p>Abstract</p> <p>Background</p> <p>To compare non coplanar field (NCF) with coplanar field (CF) -intensity-modulated radiotherapy (IMRT) planning for ethmoid cancer.</p> <p>Methods</p> <p>Seven patients treated with NCF IMRT for ethmoid cancer were studied. A CF IMRT optimization was prepared with the same constraints as for the NCF treatment. The maximum point doses (D max) obtained for the different optic pathway structures (OPS) should differ no more than 3% from those achieved with the NCF IMRT plan. The distribution of the dose in the target volume and in the critical structures was compared between the two techniques, as well as the Conformity (CI) and the Homogeneity Indexes (HI) in the target volume.</p> <p>Results</p> <p>We noted no difference between the two techniques in the OPS for the D1, D2, and D5%, in the inner ear and controlateral lens for the average Dmax, in the temporo-mandibular joints for the average mean dose, in the cord and brainstem for the average D1%. The dose-volume histograms were slightly better with the NCF treatment plan for the planning target volume (PTV) with a marginally better HI but no impact on CI. We found a great improvement in the PTV coverage with the CF treatment plan for two patients with T4 tumors.</p> <p>Conclusion</p> <p>IMRT is one of the treatment options for ethmoid cancer. The PTV coverage is optimal without compromising the protection of the OPS. The impact of non coplanar versus coplanar set up is very slight.</p

Intelligent Mobile Applications: A Systematic Mapping Study

Smart mobiles as the most affordable and practical ubiquitous devices participate heavily in the enhancement of our daily life by the use of many convenient applications. However, the significant number of mobile users in addition to their heterogeneity (different profiles and contexts) obligates developers to enhance the quality of their apps by making them more intelligent and more flexible. This is realized mainly by analyzing mobile user’s data. Machine learning (ML) technology provides the methodology and techniques needed to extract knowledge from data to facilitate decision-making. Therefore, both developers and researchers affirm the benefits of combining ML techniques and mobile technology in several application fields as e-health, e-learning, e-commerce, and e-coaching. Thus, the purpose of this paper is to have an overview of the use of ML techniques in the design and development of mobile applications. Therefore, we performed a systematic mapping study of papers published on this subject in the period between 1 January 2007 and 31 December 2019. A total number of 71 papers were selected, studied, and analyzed according to the following criteria, year, sources and channel of publication, research type, and methods, kind of collected data, and finally adopted ML models, tasks, and techniques

A Fuzzy Logic Based Set of Measures for Software Project Similarity: Validation and Possible Improvements

The software project similarity attribute has not yet been the subject of in-depth study, even though it is often used when estimating software development effort by analogy. Among the inadequacies identified (Shepperd et al.) in most of the proposed measures for the software project similarity attribute, the most critical is that they are used only when the software projects are described by numerical variables (interval, ratio or absolute scale). However, in practice, many factors which describe software projects, such as the experience of programmers and the complexity of modules, are measured in terms of an ordinal (or nominal) scale composed of qualifications such as `very low&apos;, `low&apos; and `high&apos;. To overcome this limitation, we propose a set of new measures for similarity when the software projects are described by categorical data. These measures are based on fuzzy logic: the categorical data are represented by fuzzy sets and the process of computing the various measures uses fuzzy reasoning. In this work, the proposed measures are validated by means of an axiomatic validation approach, using a set of axioms representing our intuition about the similarity attribute and verifying whether or not each measure contradicts any of the axioms. We also present in this paper the results of an empirical validation of our similarity measures, based on the COCOMO&apos;81 database