Generalized Agile Estimation Method

Agile cost estimation process always possesses research prospects due to lack of algorithmic approaches for estimating cost, size and duration. Existing algorithmic approach i.e. Constructive Agile Estimation Algorithm (CAEA) is an iterative estimation method that incorporates various vital factors affecting the estimates of the project. This method has lots of advantages but at the same time has some limitations also. These limitations may due to some factors such as number of vital factors and uncertainty involved in agile projects etc. However, a generalized agile estimation may generate realistic estimates and eliminates the need of experts. In this paper, we have proposed iterative Generalized Estimation Method (GEM) and presented algorithm based on it for agile with case studies. GEM  based algorithm various project domain classes and vital factors with prioritization level. Further, it incorporates uncertainty factor to quantify the risk of project for estimating cost, size and duration. It also provides flexibility to project managers for deciding on number of vital factors, uncertainty level and project domains thereby maintaining the agility

Definition of the on-time delivery indicator in rapid software development

Rapid software development (RSD) is an approach for developing software in rapid iterations. One of the critical success factors of an RSD project is to deliver the product releases on time and with the planned features. In this paper, we elaborate an exploratory definition of the On-Time Delivery strategic indicator in RSD based on the literature and interviews with four companies. This indicator supports decision-makers to detect development problems in order to avoid delays and to estimate the additional time needed when requirements, and specifically quality requirements, are considered.Peer ReviewedPostprint (author's final draft

Three Levels of Agile Planning in a Software Vendor Environment

There is a misconception that agile development requires minimal planning effort. In reality, an agile approach for market-driven software development requires highly disciplined, reliable, and accurate planning practices to swiftly plan and develop high value innovations in a software vendor environment. This study investigated a highly successful international software vendor based in Melbourne, Australia to provide a case study on agile planning practices. Five planning practices which were identified underlay successful agile software development for software vendors. These planning practices were driven by agile concepts such as adaptation, self-organizing, cross-functional collaboration, and empowerment/delegation. We constructed a conceptual framework for agile planning practices APP (agile planning practices) Framework illustrating the three levels of agile planning

Identifying challenges in quantifying uncertainty: Case study in infrared thermography

Complex engineering systems present a wealth of uncertainties concerning aspects ranging from performance measurements to maintainability and through-life characteristics. A quantifiable understanding of these uncertainties is vital to system optimisation and plays a key role in decision-making processes for manufacturing organisations worldwide; impacting profit, product availability and manufacturing efficiency. The aim of this paper is to examine challenges and complications that arise when quantifying uncertainties in complex engineering systems that rely on expert opinion. A thermographic inspection system is utilised as a use case. Contractor-client and supervisor-maintainer relationships are examined. Key challenges highlighted involve accurate depiction of error margins and corresponding uncertainties of components where data is only heuristically obtainable, as well as the influence of environmental conditions and skill of the maintainer

Investigating the relationship between software process improvement, situational change, and business success in software SMEs

While we have learned a great deal from Software Process Improvement (SPI) research to date, no earlier study has been designed from the outset to examine the relationship between SPI and business success in software development small- to- medium- sized companies (software SMEs). Since business processes are generally acknowledged as having an important role to play in supporting business success, it follows that the software development process (a large and complex component of the overall business process) has an important contribution to make in supporting business success in software development companies. However, to date we have very little evidence regarding the role of SPI in supporting business success, especially for software SMEs. The need for SPI is dependent on the extent of situational change in a software development setting, and therefore any examination of the relationship between SPI and business success would be deficient if it did not also examine the extent of situational change. Therefore, this thesis describes a novel approach to examining SPI, situational change and business success in software development companies. Furthermore, having discharged this new approach to 15 software SMEs, this thesis makes the important new discovery that the amount of SPI implemented in a software SME is positively associated with the extent of business success – especially when the degree of situational change is taken into account. This thesis describes the first published study to examine the relationship between SPI, situational change and business success in software SMEs. The findings suggest that there are business benefits to implementing SPI in software SMEs, with the degree of situational change being an important factor informing SPI initiatives. Furthermore, this research has yielded valuable new insights into the nature of SPI, situational change and business success in software SMEs

Towards a software development methodology for projects in higher education institutions

All educational institutions in the United States have certain particularities that differentiate them from many other public and private institutions. Some of these particularities include, among many others: academic year cycles that set very specific constraints and hard deadlines to the delivery of any tangible and intangible projects the institution is trying to accomplish; an always changing population of constituents that will be associated with the institution for a limited amount of time; and federal and state laws that are always evolving and that require the institutions to promptly act and adapt to fulfill the expectations set, in order to avoid severe lawsuits and fines. As any other teams working in projects for educational institutions, software development teams are also heavily constrained by these particularities. This makes the adoption of Software Development Methodologies that perfectly fit other industries a daunting challenge, if not almost impossible, for these teams. Software development teams in higher education are always in the need of finding a way to adapt to these challenges and efficiently perform their projects in order to address the rapid changes occurring not only in the education sector, but also in the technology industry in general. The purpose of the research in this thesis was to identify opportunities and challenges of software development methodologies used in higher education and to recommend a software development methodology to be used by software development teams working for those institutions

Learning Interaction Primitives for Biomechanical Prediction

abstract: This dissertation is focused on developing an algorithm to provide current state estimation and future state predictions for biomechanical human walking features. The goal is to develop a system which is capable of evaluating the current action a subject is taking while walking and then use this to predict the future states of biomechanical features. This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution over the weights of a set of basis functions. The prediction properties of Bayesian Interaction Primitives were utilized to predict real-time foot forces from a 9 degrees of freedom IMUs mounted to a subjects tibias. This method shows that real-time human biomechanical features can be predicted and have a promising link to real-time controls applications.Dissertation/ThesisMasters Thesis Electrical Engineering 201

QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems

Artificial Intelligence techniques can be used to classify a patient's physical activities and predict vital signs for remote patient monitoring. Regression analysis based on non-linear models like deep learning models has limited explainability due to its black-box nature. This can require decision-makers to make blind leaps of faith based on non-linear model results, especially in healthcare applications. In non-invasive monitoring, patient data from tracking sensors and their predisposing clinical attributes act as input features for predicting future vital signs. Explaining the contributions of various features to the overall output of the monitoring application is critical for a clinician's decision-making. In this study, an Explainable AI for Quantitative analysis (QXAI) framework is proposed with post-hoc model explainability and intrinsic explainability for regression and classification tasks in a supervised learning approach. This was achieved by utilizing the Shapley values concept and incorporating attention mechanisms in deep learning models. We adopted the artificial neural networks (ANN) and attention-based Bidirectional LSTM (BiLSTM) models for the prediction of heart rate and classification of physical activities based on sensor data. The deep learning models achieved state-of-the-art results in both prediction and classification tasks. Global explanation and local explanation were conducted on input data to understand the feature contribution of various patient data. The proposed QXAI framework was evaluated using PPG-DaLiA data to predict heart rate and mobile health (MHEALTH) data to classify physical activities based on sensor data. Monte Carlo approximation was applied to the framework to overcome the time complexity and high computation power requirements required for Shapley value calculations.Comment: This work has been submitted to the ELSEVIER for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Strategies in Software Development Effort Estimation

Software development effort estimating has notoriously been the Achilles heel of the software planning process. Accurately evaluating the effort required to accomplish a software change continues to be problematic, especially in Agile software development. IT organizations and project managers depend on estimation accuracy for planning software deliveries and cost determination. The purpose of this multiple case qualitative study was to identify strategies used by software development professionals in providing accurate effort estimations to stakeholders. The planning fallacy served as the studyâs conceptual framework. The participants were 10 software development professionals who were actively engaged in delivering estimates of effort on software development requests in South Texas in the United States. Data were collected from 10 software development professionals in 5 different organizations. Additionally, 23 organizational documents were gathered and reviewed. Thematic analysis was used to identify codes and themes. Prominent themes were (a) defining and decomposing requirements, (b) referencing historical data, (c) identifying risks and unknowns, and (d) fostering communication, collaboration, and a consensus. A key recommendation is for software developers to ensure requirements are defined and decomposed by evaluating the request and breaking the request into manageable pieces to understand the effort required to complete the task. Implications for positive social change include improving morale, work-life balance, alignment of expectations, and software quality