Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

Lean Construction in a Real Estate Project – A Case Study

More than 70% of construction projects are over budget and delivered beyond thebudgeted time. To overcome the same, the concept of lean integrated project deliverycame into existence. Lean culture focuses on customer by empowering employees. Itinvolves in converting waste into value by reducing waste, improving communication, andpromoting teamwork integration through a common set of tools and techniques. Aim oflean construction is to achieve the affordable cost with optimum utilization of man,material and machinery. Wastes in construction industry which do not add value are supplyin excess materials in inventory for raw material, work in progress, finished goods etc.Waiting for preceding activity, over processing and defects can be reduced through leanconstruction. This paper deals with lean construction techniques for identifying the wastein a construction project and eliminating the same. . In this project estimation of a buildingis done to find cost of the project with respect to specifications and compared thetraditional methods used and lean construction techniques to reduce the cost and improvethe quality in less time with lower cost to satisfy the customer.

Models of projects portfolio formation in construction business

The paper presents models and methods of projects portfolio formation in the construction business. The company’s strategy success depends on the projects which an organization includes in its portfolio to achieve strategic goals. Nowadays companies often use methods of individual project estimation and selection to include in their portfolio of projects. However, such approach does not consider the effect from an individual project on the whole portfolio financial result and the total risk as well as the specificities of the construction industry portfolio formation. There is usually a set of projects for evaluation and selection and, as a consequence, there are several potential portfolio combinations for a company to choose. Construction business is project-oriented and a correct choice of a certain project and a portfolio with the best combination of projects is a strategically important management element. Models and methods of portfolio formation and management will help to make a choice. The paper presents specific features of portfolio in the construction area which should be considered in the portfolio methodology formation. The comparative analysis of mathematical models for project evaluation and portfolio selection in the construction business was provided. The profiles of companies to implement different models were determined. The results of the research will be useful for scientists and practitioners to choose a model for application or adaption to a company’s needs.

A Data Extraction Algorithm from Open Source Software Project Repositories for Building Duration Estimation Models: Case Study of Github

Software project estimation is important for allocating resources and planning a reasonable work schedule. Estimation models are typically built using data from completed projects. While organizations have their historical data repositories, it is difficult to obtaintheir collaboration due to privacy and competitive concerns. To overcome the issue of public access to private data repositories this study proposes an algorithm to extract sufficient data from the GitHub repository for building duration estimation models. More specifically, this study extracts and analyses historical data on WordPress projects to estimate OSS project duration using commits as an independent variable as well as an improved classification of contributors based on the number of active days for each contributor within a release period. The results indicate that duration estimation models using data from OSS repositories perform well and partially solves the problem of lack of data encountered in empirical research in software engineering.

Development Cycle Modeling: Resource Estimation

This paper presents results produced by a domain-independent system development model that enables objective and quantitative calculation of certain development cycle characteristics. The presentation recounts the model’s motivation and includes an outline of the model’s structure. The outline shows that the model is constructive. As such, it provides an explanatory mechanism for the results that it produces, not just a representation of qualitative observations or measured data. The model is a Statistical Agent-based Model of Development and Evaluation (SAbMDE); and it appears to be novel with respect to previous design theory and methodology work. This paper focuses on one development cycle characteristic: resource utilization. The model’s resource estimation capability is compared to Boehm’s long-used software development estimation techniques. His Cone of Uncertainty (COU) captures project estimation accuracy empirically at project start but intuitively over a project’s duration. SAbMDE calculates estimation accuracy at start up and over project duration; and SAbMDE duplicates the COU’s empirical values. Additionally, SAbMDE produces results very similar to the Constructive Cost Model (COCOMO) effort estimation for a wide range of input values.