Experimental Study Using Functional Size Measurement in Building Estimation Models for Software Project Size

This paper reports on an experiment that investigates the predictability of software project size from software product size. The predictability research problem is analyzed at the stage of early requirements by accounting the size of functional requirements as well as the size of non-functional requirements. The experiment was carried out with 55 graduate students in Computer Science from Concordia University in Canada. In the experiment, a functional size measure and a project size measure were used in building estimation models for sets of web application development projects. The results show that project size is predictable from product size. Further replications of the experiment are, however, planed to obtain more results to confirm or disconfirm our claim

Using Data Mining to Identify COSMIC Function Point Measurement Competence

Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need

Strategic Knowledge Measurement and Management

Knowledge and intellectual capital are now recognized as vital resources for organizational survival and competitive advantage. A vast array of knowledge measures has evolved, spanning many disciplines. This chapter reviews knowledge measures focusing on groups of individuals (such as teams, business and organizations), as they reflect the stock or flow of knowledge, as well as enabling processes that enhance knowledge stocks and flows. The chapter emphasizes the importance of organizational value chains, pivotal talent pools and the link between knowledge and competitive success, in understanding the significance of today’s knowledge measures, and opportunities for future research and practice to enhance them

Lean Management Principles to the Creation of Postpartum Hemorrhage Care Bundles

Postpartum hemorrhage (PPH) is the number one cause of pregnancy-related death in the US. The Maternity local improvement team (LIT), co-led by an Obstetrician and Board Certified Clinical Nurse Specialist found that each month the maternity unit averages 40 PPHs with 1-2 resulting in an emergency. Over a 6-month period, the LIT decreased response time for emergencies significantly. Supply retrieval time decreased by 99.9%, MD response time decreased by 81%, and Family Centered Care increased by 100%. They recently turned their attention to prevention. Given the lack of literature on preventing PPH in postpartum units, the team developed a PPH prevention bundle-a small set of evidence-based interventions enhancing teamwork and communication to improve patient outcomes. Dr. Crowe as the national lead for benchmarking obstetrical adverse events in the Solutions for Patient Safety collaborative will track the success of the PPH bundle, which could become the first national standard in prevention of PPH requiring a blood transfusion. The team targeted 100% compliance to bundle elements, with an ultimate goal of decreasing need for transfusion. Many problems have been encountered along the way, such as RN handoffs from Labor and Delivery as well as having appropriate staff to help new mothers to the bathroom for the first time, but the team has worked through them one-by-one. Through simulation training over a 6-month period, 100 RNs, MDs, and family representatives simulated the bundle approach. The PPH Prevention Bundle could become the first national standard in prevention of PPHs on a postpartum unit

What are the Most Important Classes of Information Systems for eSourcing Service Providers? Experiences from Three Case Studies in the Chinese eSourcing Market

Information and Communications Technology (ICT)-enabled international sourcing of software-intensive systems and services (eSourcing) is increasingly used as a means of adding value, reducing costs, sharing risks, and achieving strategic aims. To maximally reap the benefits from eSourcing and mitigate the risks, providers and clients have to be aware of and build capabilities for the entire eSourcing life-cycle. China is in a remarkable position to become a superpower for eSourcing service provisioning within the next 10 years. Yet, the extant literature does not offer a comprehensive enough guidance for eSourcing management in the Chinese context. This research project will probe the eSourcing life-cycle in Information and Communications Technology Outsourcing (ICTO), Business Process Outsourcing (BPO), and Knowledge Process Outsourcing (KPO) contexts. It will provide as generalizable scientific knowledge as possible concerning the most important business practices and classes of information systems for eSourcing service providers from the viewpoint of service provisioning, breakdown recovery, and the redesign of the eSourcing life-cycle

VAF factor influence on the accuracy of the effort estimation provided by modified function points methods

The paper presents the Function Points (FP) method, which can be used for a preliminary effort estimation using limited information. Despite the potential for early use of the effort estimation, FP provides meaningful and relatively accurate results. The authors aimed to design Modified Function Points (MFP) methods based on regression model and analyze the influence of Value Adjustment Factor (VAF) on the estimation accuracy of the development effort. For research purposes was selected the ISBSG dataset. Subsequently, the original dataset was reduced according to data requirements and divided into two parts the training and the testing section (in ratio 2:1). The presented analysis was processed as a preparatory phase for further research in this area. Matlab toolboxes were used for the design and verification of discussed algorithms. © 2018, Danube Adria Association for Automation and Manufacturing, DAAAM. All rights reserved

Practical Approaches to Biological Network Discovery

This dissertation addresses a current outstanding problem in the field of systems biology, which is to identify the structure of a transcriptional network from high-throughput experimental data. Understanding of the connectivity of a transcriptional network is an important piece of the puzzle, which relates the genotype of an organism to its phenotypes. An overwhelming number of computational approaches have been proposed to perform integrative analyses on large collections of high-throughput gene expression datasets to infer the structure of transcriptional networks. I put forth a methodology by which these tools can be evaluated and compared against one another to better understand their strengths and weaknesses. Next I undertake the task of utilizing high-throughput datasets to learn new and interesting network biology in the pathogenic fungus Cryptococcus neoformans. Finally I propose a novel computational method for mapping out transcriptional networks that unifies two orthogonal strategies for network inference. I apply this method to map out the transcriptional network of Saccharomyces cerevisiae and demonstrate how network inference results can complement chromatin immunoprecipitation: ChIP) experiments, which directly probe the binding events of transcriptional regulators. Collectively, my contributions improve both the accessibility and practicality of network inference methods

Investigating Patient Outcome Measures in Mental Health

This report examines the feasibility of incorporating patient outcomes in mental health into a productivity measure. It examines which outcome measures are most commonly used in mental health, the practical issues about collecting these outcome measures, whether they can be converted into a generic measure, whether there is a time series of data available, and whether the data exists to examine changes in the mix of treatments over time. The criteria that were assumed to be important for an outcome measure to be included in a productivity index, were that it should have wide coverage, should be routinely collected, could readily be linked to activity data, could potentially be converted to a generic outcome measure, and would be available as a time-series. The report focuses predominantly on mental health outcomes within the working age population. Literature searches on outcome measurement in mental health covered numerous databases and retrieved over 1500 records. Around 170 full papers were obtained.

Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review

Context The International Software Benchmarking Standards Group (ISBSG) maintains a software development repository with over 6000 software projects. This dataset makes it possible to estimate a project s size, effort, duration, and cost. Objective The aim of this study was to determine how and to what extent, ISBSG has been used by researchers from 2000, when the first papers were published, until June of 2012. Method A systematic mapping review was used as the research method, which was applied to over 129 papers obtained after the filtering process. Results The papers were published in 19 journals and 40 conferences. Thirty-five percent of the papers published between years 2000 and 2011 have received at least one citation in journals and only five papers have received six or more citations. Effort variable is the focus of 70.5% of the papers, 22.5% center their research in a variable different from effort and 7% do not consider any target variable. Additionally, in as many as 70.5% of papers, effort estimation is the research topic, followed by dataset properties (36.4%). The more frequent methods are Regression (61.2%), Machine Learning (35.7%), and Estimation by Analogy (22.5%). ISBSG is used as the only support in 55% of the papers while the remaining papers use complementary datasets. The ISBSG release 10 is used most frequently with 32 references. Finally, some benefits and drawbacks of the usage of ISBSG have been highlighted. Conclusion This work presents a snapshot of the existing usage of ISBSG in software development research. ISBSG offers a wealth of information regarding practices from a wide range of organizations, applications, and development types, which constitutes its main potential. However, a data preparation process is required before any analysis. Lastly, the potential of ISBSG to develop new research is also outlined.Fernández Diego, M.; González-Ladrón-De-Guevara, F. (2014). Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review. Information and Software Technology. 56(6):527-544. doi:10.1016/j.infsof.2014.01.003S52754456