Search-based Automatic Repair for Fairness and Accuracy in Decision-making Software.

Decision-making software mainly based on Machine Learning (ML) may contain fairness issues (e.g., providing favourable treatment to certain people rather than others based on sensitive attributes such as gender or race). Various mitigation methods have been proposed to automatically repair fairness issues to achieve fairer ML software and help software engineers to create responsible software. However, existing bias mitigation methods trade accuracy for fairness (i.e., trade a reduction in accuracy for better fairness). In this paper, we present a novel search-based method for repairing ML-based decision making software to simultaneously increase both its fairness and accuracy. As far as we know, this is the first bias mitigation approach based on multi-objective search that aims to repair fairness issues without trading accuracy for binary classification methods. We apply our approach to two widely studied ML models in the software fairness literature (i.e., Logistic Regression and Decision Trees), and compare it with seven publicly available state-of-the-art bias mitigation methods by using three different fairness measurements. The results show that our approach successfully increases both accuracy and fairness for 61% of the cases studied, while the state-of-the-art always decrease accuracy when attempting to reduce bias. With our proposed approach, software engineers that previously were concerned with accuracy losses when considering fairness, are now enabled to improve the fairness of binary classification models without sacrificing accuracy.

Mutation analysis for evaluating code translation.

Source-to-source code translation automatically translates a program from one programming language to another. The existing research on code translation evaluates the effectiveness of their approaches by using either syntactic similarities (e.g., BLEU score), or test execution results. The former does not consider semantics, the latter considers semantics but falls short on the problem of insufficient data and tests. In this paper, we propose MBTA (Mutation-based Code Translation Analysis), a novel application of mutation analysis for code translation assessment. We also introduce MTS (Mutation-based Translation Score), a measure to compute the level of trustworthiness of a translator. If a mutant of an input program shows different test execution results from its translated version, the mutant is killed and a translation bug is revealed. Fewer killed mutants indicate better code translation. MBTA is novel in the sense that mutants are compared to their translated counterparts, and not to their original program's translation. We conduct a proof-of-concept case study with 612 Java-Python program pairs and 75,082 mutants on the code translators TransCoder and j2py to evaluate the feasibility of MBTA. The results reveal that TransCoder and j2py fail to translate 70.44% and 70.64% of the mutants, respectively, i.e., more than two-thirds of all mutants are incorrectly translated by these translators. By analysing the MTS results more closely, we were able to reveal translation bugs not captured by the conventional comparison between the original and translated programs.

Inferring test models from user bug reports using multi-objective search.

Bug reports are used by software testers to identify abnormal software behaviour. In this paper, we propose a multi-objective evolutionary approach to automatically generate finite state machines (FSMs) based on bug reports written in natural language, to automatically capture incorrect software behaviour. These FSMs can then be used by testers to both exercise the reported bugs and create tests that can potentially reveal new bugs. The FSM generation is guided by a Multi-Objective Evolutionary Algorithm (MOEA) that simultaneously minimises three objectives: size of the models, number of unrealistic states (over-generalisation), and number of states not covered by the models (under-generalisation). We assess the feasibility of our approach for 10 real-world software programs by exploiting three different MOEAs (NSGA-II, NSGA-III and MOEA/D) and benchmarking them with the baseline tool KLFA. Our results show that KLFA is not practical to be used with real-world software, because it generates models that over generalise software behaviour. Among the three MOEAs, NSGA-II obtained significantly better results than the other two for all 10 programs, detecting a greater number of bugs for 90% of the programs. We also studied the differences in quality and model performance when MOEAs are guided by only two objectives rather than three during the evolution. We found that the use of under-approximation (or over-approximation) and size as objectives generates infeasible solutions. On the other hand, using as objectives over-approximation and under-approximation generates feasible solutions yet still worse than those obtained using all three objectives for 100% of the cases. The size objective acts as a diversity factor. As a consequence, an algorithm guided by all three objectives avoids local optima, controls the size of the models, and makes the results more diverse and closer to the optimal Pareto set.

'There need to be a balance': Mental health nurses' perspectives on medication education in university and clinical practice.

Working with medication is an important role of the mental health nurse. However, little research has focused on staff nurses' perspectives on where the responsibility lies for preparing student nurses for safe, competent medication management. This study investigated mental health nurses' perspectives on medication education. An interpretive descriptive approach was used. Two focus groups were conducted, and data were analysed using inductive content analysis. It was found that participants embraced a medical approach to servicer user care, with less positive attitudes demonstrated towards psychosocial approaches. There were also tensions expressed between clinical practice and the university, with uncertainty voiced about whose responsibility it was to educate students about medication management. It is important that both environments complement each other in order to enhance the student nurse educational experience. While mental health nurses should be educated in this area to practice in a safe and competent manner, it is also key that a holistic approach to care is considered.

"A great stress among students" - mental health nurses' views of medication education: A qualitative descriptive study.

BACKGROUND: Registered mental health nurses report dissatisfaction with the level of medication knowledge demonstrated by undergraduate nurses. However, little is known about which particular areas staff nurses are concerned about, and how they feel education can be enhanced in both academic and clinical settings. OBJECTIVE: To investigate the views of staff nurses on the delivery of medication education to undergraduate mental health nurses. DESIGN: A qualitative descriptive design was adopted. METHODS: Two focus groups were held with registered nurses in two acute mental health facilities. Data were analysed using qualitative content analysis. RESULTS: The first theme reports on the difficulties staff nurses observed with both undergraduate and newly qualified nurses around medication. It was noted that these individuals had difficulties interpreting medication charts/Kardexes, and were unable to provide medication-related education to service users. The second theme reports on strategies to enhance medication education, as recommended by participants. It was suggested that more practical education should be delivered in academic settings, with a focus on simulation and presentations from clinical staff. In the clinical settings, it was suggested that preceptors should provide education at less busy times on the ward. CONCLUSIONS: This study gives insight into areas in which education needs to be strengthened, in order to improve the medication knowledge of undergraduate and newly qualified nurses. Further research is needed to develop evidence-based strategies to enhance this education.