A project effort estimation study

Accurate software effort estimation is one of the key factors to a successful project by making a better software project plan. To improve the estimation accuracy of software effort, many studies usually aimed at proposing novel effort estimation methods or combining several approaches of the existing effort estimation methods. However, those researches did not consider the distribution of historical software project data which is an important part impacting to the effort estimation accuracy. In this paper, to improve effort estimation accuracy by least squares regression, we propose a data partitioning method by the accuracy measures, MRE and MER which are usually used to measure the effort estimation accuracy. Furthermore, the empirical experimentations are performed by using two industry data sets (the ISBSG Release 9 and the Bank data set which consists of the project data performed in a bank in Korea).

Effort estimation models have been long studied in software engineering research. Effort estimation models help organizations and individuals plan and track progress of their software projects and individual tasks to help plan delivery milestones better. Towards this end, there is a large body of work that has been done on effort estimation for projects but little work on an individual checkin (Pull Request) level. In this paper we present a methodology that provides effort estimates on individual developer check-ins which is displayed to developers to help them track their work items. Given the cloud development infrastructure pervasive in companies, it has enabled us to deploy our Pull Request Lifetime prediction system to several thousand developers across multiple software families. We observe from our deployment that the pull request lifetime prediction system conservatively helps save 44.61% of the developer time by accelerating Pull Requests to completion.

Over the past ten couple of years, there is a variety of effort models proposed by academicians and practitioners at early stage of software development life cycle. Some addressed that efforts could be predicted using Lines of Codes(LOC) and COCOMO, others emphasized that it could be made using Function Point Analysis(FPA) or others. The study seeks to develop a model that estimates software effort by studying and analyzing small and medium scale application software. To develop such a model, 50 completed software projects are collected from a software company. With the sample data, design team factors are identified and extracted. By applying them to simple regression analyses, a prediction of software of effort estimates with accuracy of MMRE = 9% was constructed. The results give several benefits. First, the estimation problems are minimized due to the simple procedure used in identifying those factors. Second, the predicted software projects are only limited to a specific environment rather than being based upon industry environment. We believe the accuracy of effort estimates can be improved. According to the results analyzed, the work shows that it is possible to build up simple and useful prediction model based on data extracted at the early stage of software development life cycle. We hope this model can provide valuable ideas and suggestions for project designers for planning and controlling software projects in near future.

Software development effort estimation is one of the most crucial activities in software engineering. Effort estimation permits managers and software engineers to anticipate, forecast, and precisely quote the schedule, budget and manpower requirements. By accurately estimating the effort; software projects can be saved from under run or over run. In this paper we have summarized and then analyzed the past work of software effort estimation in a systematic way. 10 researches were surveyed and explained briefly that how they are contributing towards solving the effort estimation problem in terms of time, cost or test. It also emphasizes that various effort estimation models have different pros and cons and can be used in different context on basis of different types of historical data. The survey discovered the most popular models used for effort prediction are supervised learning algorithms. The trends identified through this survey can help in exploring the potential research areas.

Estimation support by lexical analysis of requirements documents

The effort involved in developing a software product plays an important role in determining the success or failure. In the context of developing software using object oriented methodologies, traditional methods and metrics were extended to help managers in effort estimation activity. Software project managers require a reliable approach for effort estimation. It is especially important during the early stage of the software development life cycle. In this paper, the main goal is to estimate the cost of various software projects using class point approach and optimize the parameters using six types of adaptive regression techniques such as multi-layer perceptron, multivariate adaptive regression splines, projection pursuit regression, constrained topological mapping, K nearest neighbor regression and radial basis function network to achieve better accuracy. Also a comparative analysis of software effort estimation using these various adaptive regression techniques has been provided. By estimating the effort required to develop software projects accurately, we can have softwares with acceptable quality within budget and on planned schedules.

Software projects typically require effort estimation from the software development life cycle (SDLC) staff at the commencement of the software development activities. Inaccuracies in effort estimation of software projects early on in the SDLC continues to be a perennial problem. Typically, improved estimates for software projects are provided under circumstances where requirements engineering (RE) is completed. RE provides a key reference in the form of requirements for the estimation process. However, RE by itself may consume significant effort demanding the RE phase to be estimated as the first step in the SDLC. It is difficult to establish RE estimates, as RE usually has no tangible reference on which estimates can be based. This paper provides a practical estimation method for large and complex RE processes. We demonstrate using a case study snapshot in a large software organization that the method provides improved effort estimates for the RE process.

One of the pillars for sound Software Project Management is reliable effort estimation. Therefore it is important to fully identify what are the fundamental factors that affect an effort estimate for a new project and how these factors are inter-related. This paper describes a case study where a Bayesian Network model to estimate effort for healthcare software projects was built. This model was solely elicited from expert knowledge, with the participation of seven project managers, and was validated using data from 22 past finished projects. The model led to numerous changes in process and also in business. The company adapted their existing effort estimation process to be in line with the model that was created, and the use of a mathematically-based model also led to an increase in the number of projects being delegated to this company by other company branches worldwide.

Software project effort estimation is a major process in software development cycle. This process helps in decision making in resource allocation and distribution. In this work, a new effort estimation clustering method based on estimation maximization soft-clustering unsupervised machine learning algorithm is proposed. This model classifies any software project into one of four categories. An enterprise will accept to develop a software project if this project is clustered into a class that requires resources equal or less than the enterprises resources. The new model helps in decision making process in one hand and helps consumers in assigning projects to a developing enterprise in the other hand. COCOMO dataset has been used to implement, deploy and test the model. The propose model has been compared with K-means algorithm to show the differences between soft and hard clustering. The paper results show that soft-clustering has the ability to estimate efforts like any supervised machine learning algorithms.

SMP is a project management tool specifically designed for process-oriented software projects. It integrates six project management functions: process modelling, effort estimation, planning, tracking, control and measurement. Their integration is based on three basic principles: i) a project plan should follow from a process model; ii) effort estimation should be based on project historical data; iii) control should be based on units that can be tracked across several phases and activities. SMP is inspired from Watt's Humphrey personal software process, which has been lifted to deal with large projects instead of personal projects.

A critical activity in project planning, especially in business process modeling (BPM) projects, is effort estimation. It involves several dimensions such as business domain complexity, team and technology characteristics, turning estimation into a difficult and inaccurate task. In order to reduce this difficulty, background knowledge about past projects is typically applied; however, it is too costly to be carried out manually. On the other hand, Data Mining enables the automatic extraction of new nontrivial and useful knowledge from existing data. This paper presents a new approach for BPM project effort estimation using data mining through clustering technique. This approach was successfully applied to real data

Objective. The objective of this paper is to provide a framework for effort management in software projects to increase effort estimation accuracy. Method. We applied a multimethodological approach employing a case study and constructive research. Results. Based on the case study on four earlier proposed frameworks related to effort management and three popular process maturity assessment models, we constructed a framework to manage effort in a software project in a proactive manner, yet fulfilling the requirements of the assessment models. Conclusions. A project’s software engineering process involves various functions which have an effect on the success of effort estimation. Two approaches, proactive and reactive, can be taken to manage effort in a software project. In this paper, a case study is conducted regarding both approaches. Based on the case study, we provide a new framework for effort management in software projects to increase effort estimation accuracy.

Estimates of effort and duration for a new software project often have to be adjusted to deal with an imposed target delivery date or a constraint on staffing. Estimating methods assume an effort/duration trade-off relationship based mostly on theory or expert judgment. This paper describes a process for analyzing actual project effort and duration data which is designed to explore the trade-off relationship. I assume a reference relationship of a simple power-curve with variable power ‘N’ and use this (a) as a means of comparing the trade-off relationships assumed by four well-known estimating methods, and (b) as the basis for a process to analyze actual project data. Results are presented of applying the process to 16 sub-sets of project data. These suggest, for example, that the value of ‘N’ differs between new development projects and enhancement projects. The Web Extra presents more results for each step in the effort-duration trade-off process described in the main article.

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Effort estimation is a critical stage in the life cycle of a project. Effort underestimation may lead to product quality reduction and trust issues. This also applies to Extract Transfer Load (ETL) Big Data projects. This study aims to propose an effort estimation method related to ETL Big Data projects. A literature review was performed to analyze the key aspects affecting effort estimation for ETL Big Data projects. The output of the review was used to carry out a survey with professionals involved in such projects to identify factors and methodologies influential to ETL Big Data effort estimation. The findings from the literature and the survey results were used to propose a method that can be used for effort estimation for ETL Big Data projects. The proposed solution is to implement the current estimation method of an organization and the Constructive Cost Model (COCOMO) II method which are aligned with factors related to ETL Big Data projects. The proposed solution was evaluated using a real case study of a multinational company. The COCOMO II method was found to be slightly better than the manual estimation method currently used. Although further evaluation is required with different types of ETL Big Data projects, based on these initial findings, we can say that COCOMO II can be used for such projects.