Review of current data mining techniques used in the software effort estimation

Data Mining is a method of finding patterns from vast quantities of data and information. The data sources include databases, data centers, the internet, and other data storage forms; or data that is dynamically streaming into the network. Estimation of effort is very important in the cost estimation of a software development project, and very critical in the software life development cycle planning process. This paper offers a description of the latest data mining techniques used in estimating software effort, and these techniques are divided into two, namely: Classical and Modern, based on when they were developed and when they started to be used in business administration. The Classical techniques are the ones that have been in use for decades and are still relevant until today, while the Modern ones are the ones that have been introduced recently and have gained wide acceptance in the system. The Classical techniques are Statistical methods, Nearest Neighbours, Clustering and Regression Analysis, while Neural Networks, Rule Induction Systems and Decision Trees are included in the Modern techniques. This paper offers an overview of these strategies in terms of their features, benefits, drawbacks and use areas. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

Data Carving: Identifying and Removing Irrelevancies in the Data

Data mining has been successfully applied in the recent decades to automatically discover valuable hidden patterns from vast data. However, the current data mining approaches almost exclude business users from the process of knowledge discovery. Most of the learning algorithms operate as black-boxes , which give predictions based on some input, without giving any hint on how they make their decisions. Moreover, their output is often difficult to interpret or understand by business users.;This thesis explores how to facilitate the users\u27 engagement in the process of data analysis. We present PEEKING2, which combines a set of data reduction and data transformation techniques to create succinct summaries from raw data. In this way, users can peek at small representative data to reason over them. After removing uninformative features, PEEKING2 clusters the data combining FASTMAP projection and grid-clustering. A condensed summary of the data is then formed from the centroids of the resulted clusters. Finally, PEEKING2 extrapolates between centroids to predict the class of new instances.;PEEKING2 has been tested on Software Engineering data for software defect prediction and development effort estimation. Specifically, we have applied PEEKING2 on 10 defect data sets and 10 effort data sets from the PROMISE repository. PEEKING2 could reduce large data of 800+ rows and 20+ columns to just 10-30 rows and less than 6 columns.;To assess its predictive ability, we have compared PEEKING2 to more elaborate learners. Regarding defect prediction, PEEKING2 performed almost as well or better than Naive Bayes and Random Forest in most of the data sets. Similarly, when applied on effort estimation data, PEEKING2 outperformed or performed the same as Linear Regression and M5P in the majority of cases.;These results shows that it is possible to peek at the data without losing significant information. Consequently, we recommend PEEKING2 as a data summarization tool to assist managers and software engineers in their analysis of the project data

Application of mutual information-based sequential feature selection to ISBSG mixed data

[EN] There is still little research work focused on feature selection (FS) techniques including both categorical and continuous features in Software Development Effort Estimation (SDEE) literature. This paper addresses the problem of selecting the most relevant features from ISBSG (International Software Benchmarking Standards Group) dataset to be used in SDEE. The aim is to show the usefulness of splitting the ranked list of features provided by a mutual information-based sequential FS approach in two, regarding categorical and continuous features. These lists are later recombined according to the accuracy of a case-based reasoning model. Thus, four FS algorithms are compared using a complete dataset with 621 projects and 12 features from ISBSG. On the one hand, two algorithms just consider the relevance, while the remaining two follow the criterion of maximizing relevance and also minimizing redundancy between any independent feature and the already selected features. On the other hand, the algorithms that do not discriminate between continuous and categorical features consider just one list, whereas those that differentiate them use two lists that are later combined. As a result, the algorithms that use two lists present better performance than those algorithms that use one list. Thus, it is meaningful to consider two different lists of features so that the categorical features may be selected more frequently. We also suggest promoting the usage of Application Group, Project Elapsed Time, and First Data Base System features with preference over the more frequently used Development Type, Language Type, and Development Platform.Fernández-Diego, M.; González-Ladrón-De-Guevara, F. (2018). Application of mutual information-based sequential feature selection to ISBSG mixed data. Software Quality Journal. 26(4):1299-1325. https://doi.org/10.1007/s11219-017-9391-5S12991325264Angelis, L., & Stamelos, I. (2000). 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Making Software Cost Data Available for Meta-Analysis

In this paper we consider the increasing need for meta-analysis within empirical software engineering. However, we also note that a necessary precondition to such forms of analysis is to have both the results in an appropriate format and sufficient contextual information to avoid misleading inferences. We consider the implications in the field of software project effort estimation and show that for a sample of 12 seemingly similar published studies, the results are difficult to compare let alone combine. This is due to different reporting conventions. We argue that a protocol is required and make some suggestions as to what it should contain

Is One Hyperparameter Optimizer Enough?

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics. To address this gap in the literature, this paper applied a range of hyperparameter optimizers (grid search, random search, differential evolution, and Bayesian optimization) to defect prediction problem. Surprisingly, no hyperparameter optimizer was observed to be `best' and, for one of the two evaluation measures studied here (F-measure), hyperparameter optimization, in 50\% cases, was no better than using default configurations. We conclude that hyperparameter optimization is more nuanced than previously believed. While such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1