Design science, engineering science and requirements engineering

For several decades there has been a debate in the computing sciences about the relative roles of design and empirical research, and about the contribution of design and research methodology to the relevance of research results. In this minitutorial we review this debate and compare it with evidence about the relation between design and research in the history of science and technology. Our review shows that research and design are separate but concurrent activities, and that relevance of research results depends on problem setting rather than on rigorous methods. We argue that rigorous scientific methods separate design from research, and we give simple model for how to do this in a problem-driven way

Preliminary Survey on Empirical Research Practices in Requirements Engineering

Context and Motivation:\ud Based on published output in the premium RE conferences and journals, we observe a growing body of research using both quantitative and qualitative research methods to help understand which RE technique, process or tool work better in which context. Also, more and more empirical studies in RE aim at comparing and evaluating alternative techniques that are solutions to common problems. However, until now there have been few meta studies of the current state of knowledge about common practices carried out by researchers and practitioners in empirical RE. Also, surprisingly little has been published on how RE researchers perceive the usefulness of these best practices.\ud \ud Objective:\ud The goal of our study is to improve our understanding of what empirical practices are performed by researchers and practitioners in RE, for the purpose of understanding the extent to which the research methods of empirical software engineering are adopted in the RE community.\ud \ud Method:\ud We surveyed the practices that participants of the REFSQ conference have been using in their empirical research projects. The survey was part of the REFSQ 2012 Empirical Track.\ud \ud Conclusions:\ud We found that there are 15 commonly used practices out of a set of 27. The study has two implications: first it presents a list of practices that are commonly used in the RE community, and a list of practices that still remain to be practiced. Researchers may now make an informed decision on how to extend the practices they use in producing and executing their research designs, so that their designs get better. Second, we found that senior researchers and PhD students do not always converge in their perceptions about the usefulness of research practices. Whether this is all right and whether something needs to be done in the face of this finding remains an open question

A Unified Checklist for Observational and Experimental Research in Software Engineering (Version 1)

Current checklists for empirical software engineering cover either experimental research or case study research but ignore the many commonalities that exist across all kinds of empirical research. Identifying these commonalities, and explaining why they exist, would enhance our understanding of empirical research in general and of the differences between experimental and case study research in particular. In this report we design a unified checklist for empirical research, and identify commonalities and differences between experimental and case study research. We design the unified checklist as a specialization of the general engineering cycle, which itself is a special case of the rational choice cycle. We then compare the resulting empirical research cycle with two checklists for experimental research, and with one checklist for case study research. The resulting checklist identifies important questions to be answered in experimental and case study research design and reports. The checklist provides insights in two different types of empirical research design and their relationships. Its limitations are that it ignores other research methods such as meta-research or surveys. It has been tested so far only in our own research designs and in teaching empirical methods. Future work includes expanding the comparison with other methods and application in more cases, by others than ourselves

Novel Virtual Environment for Alternative Treatment of Children with Cerebral Palsy

Cerebral palsy is a severe condition usually caused by decreased brain oxygenation during pregnancy, at birth or soon after birth. Conventional treatments for cerebral palsy are often tiresome and expensive, leading patients to quit treatment. In this paper, we describe a virtual environment for patients to engage in a playful therapeutic game for neuropsychomotor rehabilitation, based on the experience of the occupational therapy program of the Nucleus for Integrated Medical Assistance (NAMI) at the University of Fortaleza, Brazil. Integration between patient and virtual environment occurs through the hand motion sensor “Leap Motion,” plus the electroencephalographic sensor “MindWave,” responsible for measuring attention levels during task execution. To evaluate the virtual environment, eight clinical experts on cerebral palsy were subjected to a questionnaire regarding the potential of the experimental virtual environment to promote cognitive and motor rehabilitation, as well as the potential of the treatment to enhance risks and/or negatively influence the patient’s development. Based on the very positive appraisal of the experts, we propose that the experimental virtual environment is a promising alternative tool for the rehabilitation of children with cerebral palsy.Cerebral palsy is a severe condition usually caused by decreased brain oxygenation during pregnancy, at birth or soon after birth. Conventional treatments for cerebral palsy are often tiresome and expensive, leading patients to quit treatment. In this paper, we describe a virtual environment for patients to engage in a playful therapeutic game for neuropsychomotor rehabilitation, based on the experience of the occupational therapy program of the Nucleus for Integrated Medical Assistance (NAMI) at the University of Fortaleza, Brazil. Integration between patient and virtual environment occurs through the hand motion sensor “Leap Motion,” plus the electroencephalographic sensor “MindWave,” responsible for measuring attention levels during task execution. To evaluate the virtual environment, eight clinical experts on cerebral palsy were subjected to a questionnaire regarding the potential of the experimental virtual environment to promote cognitive and motor rehabilitation, as well as the potential of the treatment to enhance risks and/or negatively influence the patient’s development. Based on the very positive appraisal of the experts, we propose that the experimental virtual environment is a promising alternative tool for the rehabilitation of children with cerebral palsy

Un diseño experimental sobre algoritmo de clasificación de elementos de modelos de procesos SPME 2.0 de la herramienta AVISPA por medio del análisis de varianza ANOVA

Introduction: This publication is the product of research: “SPEM 2.0 Process Model Metrics in the Reliability of its Visual Analysis” throughout 2019, which supports the work of a master’s degree in Systems Engineering at the University of Cauca. Objective: Rebase a process model metrics set in order to increase AVISPA reliability to support the visual analysis of SPEM 2.0 software process models. Methodology: In order to improve AVISPA, a systematic literature review had been performed to find software process model metrics that are potentially apt to be represented in AVISPA. Next, a set of assessments were performed in order to enhance visual analysis tool. Finally, an ANOVA statistical assessment was realized in order to find a variance differential between AVISPA versions by comparing their F1-Score process model elements classification values. Results: AVISPA significantly improved its general classification algorithm. Most of errors were found in SPEM 2.0 variability resolution feature and collections with duplicated elements. Multiple misclassifications still persists. Conclusion: General AVISPA process model elements classification is improved. However, some process model samples remain scattered according to ANOVA results. Originality: AVISPA is a recent solution for SPEM 2.0 software process model assessment. It's recent emergence carried to a lack of articles about software process model metrics and few works about AVISPA improvements. These are the main contributions of this paper. Limitations: The project has been widely expensive in terms of execution time, traceability with all software process model elements, and mainly to find experts in software process that can meet the research requirement

Establishing a Search String to Detect Secondary Studies in Software Engineering

Search for secondary studies is essential to establish whether the review on the intended topic has already been done, avoiding waste time. In addition, secondary studies are the inputs of a tertiary study. However, one critical step in searching for secondary studies is to elaborate a search string. The main goal of this work is to analyze search strings to establish directions to better detect secondary studies in Software Engineering (SE). We analyzed seven tertiary studies under two perspectives: (1) structure - strings' terms to detect secondary studies; and (2) field: where searching - titles alone or abstracts alone or titles and abstracts together, among others. We also performed a validation of the results found. The suitable search string for finding secondary studies in SE contain the terms "systematic review", "literature review", "systematic mapping", "mapping study", "systematic map", "meta-analysis", "survey" and "literature analysis". Furthermore, we recommend (1) researchers use the title, abstract and keywords search fields in their searches to increase studies recall; (2) researchers choose carefully their paper title, abstract and keyword terms to increase the chance of having such studies found on digital libraries

Finding an Effective Classification Technique to Develop a Software Team Composition Model

Ineffective software team composition has become recognized as a prominent aspect of software project failures. Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection. It is also believed that the technique/s used while developing a model can impact the overall results. Thus, this study aims to: 1) discover an effective classification technique to solve the problem, and 2) develop a model for composition of the software development team. The model developed was composed of three predictors: team role, personality types, and gender variables; it also contained one outcome: team performance variable. The techniques used for model development were logistic regression, decision tree, and Rough Sets Theory (RST). Higher prediction accuracy and reduced pattern complexity were the two parameters for selecting the effective technique. Based on the results, the Johnson Algorithm (JA) of RST appeared to be an effective technique for a team composition model. The study has proposed a set of 24 decision rules for finding effective team members. These rules involve gender classification to highlight the appropriate personality profile for software developers. In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models