Software Effort Prediction - A Fuzzy Logic Approach

Accuracy in the estimation of software Effort/Cost is one of the desirable criteria for any software cost estimation model. The estimation of effort or cost before the actual development of any software is the most crucial task of the present day software development project managers. Software project attributes are often measured in terms of linguistic values such as very low, low, Average, high and very high. The imprecise nature of such attributes constitutes uncertainty and vagueness in their subsequent interpretation. In this paper we propose a Fuzzy logic based model for software effort prediction. We feel that fuzzy Software cost estimation Model should be able to deal with imprecision and uncertainty associated with various parameter values. Fuzzy analogy model has been developed and validated upon student data

Ensemble missing data techniques for software effort prediction

Constructing an accurate effort prediction model is a challenge in software engineering. The development and validation of models that are used for prediction tasks require good quality data. Unfortunately, software engineering datasets tend to suffer from the incompleteness which could result to inaccurate decision making and project management and implementation. Recently, the use of machine learning algorithms has proven to be of great practical value in solving a variety of software engineering problems including software prediction, including the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper proposes a method for improving software effort prediction accuracy produced by a decision tree learning algorithm and by generating the ensemble using two imputation methods as elements. Benchmarking results on ten industrial datasets show that the proposed ensemble strategy has the potential to improve prediction accuracy compared to an individual imputation method, especially if multiple imputation is a component of the ensemble

Data accumulation and software effort prediction

BACKGROUND: In reality project managers are constrained by the incremental nature of data collection. Specifically, project observations are accumulated one project at a time. Likewise within-project data are accumulated one stage or phase at a time. However, empirical researchers have given limited attention to this perspective. PROBLEM: Consequently, our analyses may be biased. On the one hand, our predictions may be optimistic due to the availability of the entire data set, but on the other hand pessimistic due to the failure to capitalize upon the temporal nature of the data. Our goals are (i) to explore the impact of ignoring time when building cost prediction models and (ii) to show the benefits of re-estimating using completed phase data during a project. METHOD: Using a small industrial data set of sixteen software projects from a single organization we compare predictive models developed using a time-aware approach with a more traditional leave-one-out analysis. We then investigate the impact of using requirements, design and implementation phase data on estimating subsequent phase effort. RESULTS: First, we find that failure to take the temporal nature of data into account leads to unreliable estimates of their predictive efficacy. Second, for this organization, prior-phase effort data could be used to improve the management of subsequent process tasks. CONCLUSION: We should collect time-related data and use it in our analyses. Failure to do so may lead to incorrect conclusions being drawn, and may also inhibit industrial take up of our research work

Predicting software project effort: A grey relational analysis based method

This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.The inherent uncertainty of the software development process presents particular challenges for software effort prediction. We need to systematically address missing data values, outlier detection, feature subset selection and the continuous evolution of predictions as the project unfolds, and all of this in the context of data-starvation and noisy data. However, in this paper, we particularly focus on outlier detection, feature subset selection, and effort prediction at an early stage of a project. We propose a novel approach of using grey relational analysis (GRA) from grey system theory (GST), which is a recently developed system engineering theory based on the uncertainty of small samples. In this work we address some of the theoretical challenges in applying GRA to outlier detection, feature subset selection, and effort prediction, and then evaluate our approach on five publicly available industrial data sets using both stepwise regression and Analogy as benchmarks. The results are very encouraging in the sense of being comparable or better than other machine learning techniques and thus indicate that the method has considerable potential.National Natural Science Foundation of Chin