| Evaluating the impact of feature selection consistency in software prediction | 0 | 0.34 | 2022 |
| Detecting privacy requirements from User Stories with NLP transfer learning models | 0 | 0.34 | 2022 |
| Design and automation of a COSMIC measurement procedure based on UML models | 0 | 0.34 | 2020 |
| Assessing the Effectiveness of Approximate Functional Sizing Approaches for Effort Estimation | 2 | 0.38 | 2020 |
| Using Bio-Inspired Features Selection Algorithms in Software Effort Estimation: A Systematic Literature Review | 0 | 0.34 | 2019 |
| Guest Editorial of Special Section on Software Engineering and Advanced Applications in Information Technology for Software-Intensive Systems. | 1 | 0.37 | 2018 |
| Dealing with Design Pattern Variants in Reverse Engineering: An Exploratory Study | 0 | 0.34 | 2018 |
| Detecting the Behavior of Design Patterns through Model Checking and Dynamic Analysis. | 6 | 0.45 | 2018 |
| Impact of Design Pattern Implementation Variants on the Retrieval Effectiveness of a Recovery Tool: An Exploratory Study | 0 | 0.34 | 2018 |
| Do software models based on the UML aid in source-code comprehensibility? Aggregating evidence from 12 controlled experiments | 2 | 0.36 | 2018 |
| Definition and evaluation of a COSMIC measurement procedure for sizing Web applications in a model-driven development environment. | 2 | 0.35 | 2018 |
| A Set of Metrics for the Effort Estimation of Mobile Apps. | 0 | 0.34 | 2017 |
| How the Use of Design Patterns Affects the Quality of Software Systems: A Preliminary Investigation | 0 | 0.34 | 2017 |
| A study on the statistical convertibility of IFPUG Function Point, COSMIC Function Point and Simple Function Point. | 3 | 0.39 | 2017 |
| Mobile App Development and Management: Results from a Qualitative Investigation. | 1 | 0.43 | 2017 |
| Simple function points for effort estimation: a further assessment. | 3 | 0.42 | 2016 |
| Supporting Mobile Development Project-Based learning by Software Project and Product Measures. | 0 | 0.34 | 2016 |
| Estimate method calls in Android apps. | 0 | 0.34 | 2016 |
| Web Effort Estimation: Function Point Analysis vs. COSMIC | 4 | 0.41 | 2016 |
| ePadEvo: A tool for the detection of behavioral design patterns | 2 | 0.36 | 2015 |
| Using Project-Based-Learning in a mobile application development course—An experience report | 8 | 0.75 | 2015 |
| From Function Points to COSMIC - A Transfer Learning Approach for Effort Estimation | 1 | 0.34 | 2015 |
| Towards automating dynamic analysis for behavioral design pattern detection | 4 | 0.37 | 2015 |
| Studying the Effect of UML-Based Models on Source-Code Comprehensibility: Results from a Long-Term Investigation. | 1 | 0.35 | 2015 |
| Documenting Design-Pattern Instances: A Family of Experiments on Source-Code Comprehensibility | 11 | 0.49 | 2015 |
| Investigating Functional and Code Size Measures for Mobile Applications: A Replicated Study | 9 | 0.43 | 2015 |
| Investigating Functional and Code Size Measures for Mobile Applications | 2 | 0.35 | 2015 |
| A fine-grained analysis of the support provided by UML class diagrams and ER diagrams during data model maintenance | 1 | 0.37 | 2015 |
| On the Experience of Using Git-Hub in the Context of an Academic Course for the Development of Apps for Smart Devices. | 3 | 0.42 | 2015 |
| COSMIC functional measurement of mobile applications and code size estimation | 7 | 0.52 | 2015 |
| Source-code comprehension tasks supported by UML design models: Results from a controlled experiment and a differentiated replication. | 3 | 0.44 | 2015 |
| On the Use of Requirements Measures to Predict Software Project and Product Measures in the Context of Android Mobile Apps: A Preliminary Study | 4 | 0.39 | 2015 |
| On The Effect Of Exploiting Gpus For A More Eco-Sustainable Lease Of Life | 3 | 0.38 | 2015 |
| Conversion from IFPUG FPA to COSMIC: Within-vs Without-Company Equations | 4 | 0.38 | 2014 |
| On the impact of UML analysis models on source-code comprehensibility and modifiability | 23 | 0.61 | 2014 |
| Exploiting prior-phase effort data to estimate the effort for the subsequent phases: a further assessment | 6 | 0.41 | 2014 |
| A Lean Approach to Estimate the Functional Size of Operating Applications | 1 | 0.39 | 2013 |
| Using tabu search to configure support vector regression for effort estimation. | 37 | 0.86 | 2013 |
| Estimating the Effort to Develop Screen Mockups | 4 | 0.42 | 2013 |
| Assessing the Effectiveness of Sequence Diagrams in the Comprehension of Functional Requirements: Results from a Family of Five Experiments | 35 | 1.06 | 2013 |
| An Early Investigation on the Contribution of Class and Sequence Diagrams in Source Code Comprehension | 3 | 0.39 | 2013 |
| Approximate COSMIC Size to Early Estimate Web Application Development Effort | 10 | 0.59 | 2013 |
| Class level fault prediction using software clustering. | 20 | 0.64 | 2013 |
| Single and Multi Objective Genetic Programming for software development effort estimation | 13 | 0.55 | 2012 |
| Does the combined use of class and sequence diagrams improve the source code comprehension?: results from a controlled experiment | 8 | 0.49 | 2012 |
| Functional versus design measures for model-driven web applications: a case study in the context of web effort estimation | 2 | 0.35 | 2012 |
| Do professional developers benefit from design pattern documentation? a replication in the context of source code comprehension | 10 | 0.46 | 2012 |
| A genetic algorithm to configure support vector machines for predicting fault-prone components | 17 | 0.62 | 2011 |
| Clustering and lexical information support for the recovery of design pattern in source code | 6 | 0.41 | 2011 |
| Investigating the use of Support Vector Regression for web effort estimation | 17 | 0.64 | 2011 |
