Application of Statistical Methods in Software Engineering: Theory and Practice

TL;DR

This paper discusses the application of various statistical methods in software engineering research, providing practical guidance and a decision tree to select appropriate techniques for data analysis in experimental studies.

Contribution

It introduces a practical decision tree for selecting statistical methods in software engineering experiments, supported by real project data demonstrations.

Findings

Effective statistical techniques for software engineering data analysis

A decision tree aids in choosing appropriate methods

Demonstrated applicability with actual software project data

Abstract

The experimental evaluation of the methods and concepts covered in software engineering has been increasingly valued. This value indicates the constant search for new forms of assessment and validation of the results obtained in Software Engineering research. Results are validated in studies through evaluations, which in turn become increasingly stringent. As an alternative to aid in the verification of the results, that is, whether they are positive or negative, we suggest the use of statistical methods. This article presents some of the main statistical techniques available, as well as their use in carrying out the implementation of data analysis in experimental studies in Software Engineering. This paper presents a practical approach proving statistical techniques through a decision tree, which was created in order to facilitate the understanding of the appropriate statistical method for each data analysis situation. Actual data from the software projects were employed to demonstrate the use of these statistical methods. Although it is not the aim of this work, basic experimentation and statistics concepts will be presented, as well as a concrete indication of the applicability of these techniques.