Software bias has emerged as a relevant issue in the latest years, in conjunction with the increasing adoption of software automation in a variety of organizational and production processes of our society, and especially in decision-making. Among the causes of software bias, data imbalance is one of the most significant issues. In this paper, we treat imbalance in datasets as a risk factor for software bias. Specifically, we define a methodology to identify thresholds for balance measures as meaningful risk indicators of unfair classification output. We apply the methodology to a large number of data mutations with different classification tasks and tested all possible combinations of balance-unfairness-algorithm.The results show that on average the thresholds can accurately identify the risk of unfair output. In certain cases they even tend to overestimate the risk: although such behavior could be instrumental to a prudential approach towards software discrimination, further work will be devoted to better assess the reliability of the thresholds.The proposed methodology is generic and it can be applied to different datasets, algorithms, and context-specific thresholds.

Bias in the data used to train decision-making systems is a relevant socio-technical issue that emerged in recent years, and it still lacks a commonly accepted solution. Indeed, the "bias in-bias out" problem represents one of the most significant risks of discrimination, which encompasses technical fields, as well as ethical and social perspectives. We contribute to the current studies of the issue by proposing a data quality measurement approach combined with risk management, both defined in ISO/IEC standards. For this purpose, we investigate imbalance in a given dataset as a potential risk factor for detecting discrimination in the classification outcome: specifically, we aim to evaluate whether it is possible to identify the risk of bias in a classification output by measuring the level of (im)balance in the input data. We select four balance measures (the Gini, Shannon, Simpson, and Imbalance ratio indexes) and we test their capability to identify discriminatory classification outputs by applying such measures to protected attributes in the training set. The results of this analysis show that the proposed approach is suitable for the goal highlighted above: the balance measures properly detect unfairness of software output, even though the choice of the index has a relevant impact on the detection of discriminatory outcomes, therefore further work is required to test more in-depth the reliability of the balance measures as risk indicators. We believe that our approach for assessing the risk of discrimination should encourage to take more conscious and appropriate actions, as well as to prevent adverse effects caused by the "bias in-bias out" problem.

The increase in the amount of structured data published on the Web using the principles of Linked Data means that now it is more likely to find resources on the Web of Data that represent real life concepts. Discovering and recommending resources on the Web of Data related to a given resource is still an open research area. This work presents a framework to deploy and execute Linked Data based recommendation algorithms to measure their accuracy and performance in different contexts. Moreover, application developers can use this framework as the main component for recommendation in various domains. Finally, this paper describes a new recommendation algorithm that adapts its behavior dynamically based on the features of the Linked Data dataset used. The results of a user study show that the algorithm proposed in this paper has better accuracy and novelty than other state-of-the-art algorithms for Linked Data.

Software repositories are analyzed to extract useful information on software characteristics. One of them is external quality. A technique used to increase software quality is automatic static analysis, by means of bug finding tools. These tools promise to speed up the verification of source code; anyway, there are still many problems, especially the high number of false positives, that hinder their large adoption in software development industry. We studied the capability of a popular bug-finding tool, FindBugs, for defect prediction purposes, analyzing the issues revealed on a repository of university Java projects. Particularly, we focused on the percentage of them that indicates actual defects with respect to their category and priority, and we ranked them. We found that a very limited set of issues have high precision and therefore have a positive impact on code external quality.