Estimating Agile Software Project Effort: An Empirical Study

This paper describes an empirical study of effort estimation in agile software development. Estimated effort and actual effort of a 46-iteration project are collected and analyzed. The results show that estimation in agile development is more accurate than that in traditional development even though agile developers still underestimate the effort. However, estimation accuracy is not improved over time as expected by agile communities

Reliability and validity in comparative studies of software prediction models

Empirical studies on software prediction models do not converge with respect to the question "which prediction model is best?" The reason for this lack of convergence is poorly understood. In this simulation study, we have examined a frequently used research procedure comprising three main ingredients: a single data sample, an accuracy indicator, and cross validation. Typically, these empirical studies compare a machine learning model with a regression model. In our study, we use simulation and compare a machine learning and a regression model. The results suggest that it is the research procedure itself that is unreliable. This lack of reliability may strongly contribute to the lack of convergence. Our findings thus cast some doubt on the conclusions of any study of competing software prediction models that used this research procedure as a basis of model comparison. Thus, we need to develop more reliable research procedures before we can have confidence in the conclusions of comparative studies of software prediction models

Identifying Effort Estimation Factors for Corrective Maintenance in Object-Oriented Systems

This research explores the decision-making process of expert estimators of corrective maintenance projects by usingqualitative methods to identify the factors that they use in deriving estimates. We implement a technique called causalmapping, which allows us to identify the cognitive links between the information that estimators use, and the estimates thatthey produce based on that information. Results suggest that a total of 17 factors may be relevant for corrective maintenanceeffort estimation, covering constructs related to developers, code, defects, and environment. This line of research aims ataddressing the limitations of existing maintenance estimation models that do not incorporate a number of soft factors, thus,achieving less accurate estimates than human experts

Benefits Quantification in IT Projects

The probability of IT project failures can be mitigated more successfully when discovered early. To support an early detection, transparency regarding a project’s cash flows shall be increased. Therefore, an appropriate analysis and calculation of a project’s costs, benefits, risks and interdependencies is inevitable. Until today, however, a method that appropriately considers these factors when estimating the ex ante project business case does not yet exist. Using the Action Design Research approach, we designed, applied and tested a practicable and integrated method of determining the monetary value of IT projects to generate generalized insights to benefits management. This method was conjointly developed by practice and academia, to ensure practical applicability while upholding scientific rigor. Furthermore, to support understandability of the method, we provide an application example

The End User Requirement for Project Management Software Accuracy

This research explains the relationship between the end user requirement and accuracy of PMS (Project Management Software). The research aims are to analyze the PMS accuracy and measuring the probability of PMS accuracy in achieving ±1% of the end user requirement. The bias statistical method will be used to prove the PMS accuracy that based on the hypothesis testing. The result indicates the PMS is still accurate to be implemented in Aceh-Indonesia area projects that using the SNI (National Indonesia Standard as current method) with the accuracy index of ±7.5%. The achievement probability of reaching the end user requirement is still low of ±21.77%. In case of the PMS, the low achievement of the end user requirement is not only caused by the low accuracy of the PMS but also caused by the amount of variability error, which is influenced by the amount of variation of the project activity. In this study, we confirm that it is necessary to reconcile both conditions between the PMS accuracy and the end user requirements

A Task-Centered, Multiple Method Approach To Teaching Fraud Risk Assessment

This manuscript provides an approach to teaching fraud risk assessment that is based on an analysis of the task and relevant research in education, cognitive psychology, and artificial intelligence. Fraud risk assessment (FRA) in financial reporting is an important and difficult task that must be performed in every financial statement audit. When auditors fail to detect fraudulent financial reporting (FFR), they are likely to become targets of shareholder and creditor litigation. Although FFR has a low occurrence rate considering the large number of financial statement audits conducted, it has a devastating impact on the investors, creditors and the profession

Pioneers of Parametrics

This paper provides a historical account of the development of the field of parametrics through information obtained during interviews of twelve pioneers of the field. Cost model developers, users, and practitioners were interviewed with the intent to capture their views on the impact between cost estimation research and practice. The individuals interviewed represent a diverse range of perspectives including academia, government, and industry. Each perspective sheds light on the areas in which the field of parametrics has had an impact and which synergies have been influential in the development of the field. The implications of the findings are discussed in light of the future challenges for the field of parametrics

On Broadening Software Development Productivity Research to Serve Better Software Engineering Management

The unresolved problems of improving software engineering management require a broader systemic approach of investigating related issues like software development productivity. The paper links software engineering management to research on software cost estimation and on factors affecting software development productivity. It examines ways for the systemic incorporation of all issues influencing a software project through application of combination of methods from diverse paradigms

New product development resource forecasting

Forecasting resource requirements for new product development (NPD) projects is essential for both strategic and tactical planning. Sophisticated, elegant planning tools to present data and inform decision-making do exist. However, in NPD, such tools run on unreliable, estimation-based resource information derived through undefined processes. This paper establishes that existing methods do not provide transparent, consistent, timely or accurate resource planning information, highlighting the need for a new approach to resource forecasting, specifically in the field of NPD. The gap between the practical issues and available methods highlights the possibility of developing a novel design of experiments approach to create resource forecasting models