Machine learning (ML) is widely used in healthcare applications to diagnose diseases, forecast disease progression, develop personalized treatment plans, and aid in drug discovery and development [1]. The development of ML applications is inherently different from other applications. Instead of explicitly coding the program's logic, ML applications learn this logic using a machine learning algorithm and provided data. Thus, faults in ML applications, as opposed to in others, can manifest in all these components, such as the application itself, incorrect use of the machine learning algorithms or libraries, and issues with data used for training. Thus, understanding these various root causes of real faults would help to develop effective testing techniques for these applications. Therefore, we analyzed 50 real-life faults from four ML healthcare applications to better understand the faults presented in this domain.

Machine Learning (ML) is the field of study that allows computers to learn from experiences without being explicitly programmed [1]. ML models are currently used in many safety-critical applications in healthcare [2]–[4] and survival analyses [5]. Thus, faults in this software can directly impact the quality of human life. In an ML application, the program logic is typically derived by a ML algorithm using the currently available data (i.e., training data) rather than explicitly being programmed [6]. Therefore, the program's behavior would evolve as it is exposed to new data. Further, healthcare ML applications are inherently complex and typically constructed by the interconnection of several components, such as data that is used to derive the logic, the ML framework that contains the algorithms used by the program, and the program itself that is written by the programmer for a specific task involved with healthcare [7]. Faults in any of these components may produce an observable incorrect output or the statistical nature of these programs may mask the incorrect output altogether, making it more challenging to understand the root causes of these failures.

The usage of social media platforms has increased tremendously in the past decade. Twitter is one of the most popular platforms for sharing opinions and feelings on various topics, organizations rely on Twitter data to analyze and gather insights for their businesses using Natural Language Processing (NLP) techniques. However, there are various challenges in analyzing such a huge volume of tweets that are in text format. One such challenge is to identify irony in tweets, which has a significant impact on analyzing sentiments. To overcome this challenge, we propose a solution that automatically recognizes the presence of irony in text and classifies the type of irony. The solution consists of two tasks: Task A performs binary classification to annotate whether irony is expressed or not in each tweet, while Task B performs multi-class classification to classify the type of irony expressed in the tweets. We used the dataset from the “SemEval-2018 Task 3: Irony detection in English tweets” challenge to train and test the proposed solution. The dataset contains 3,817 English tweets for training and 784 English tweets for testing both Tasks A and B. We developed different machine learning models by leveraging traditional classifiers, neural networks, and large language models. Our UNF-IDT (Irony Detector in Text) model developed using BERT (Bidirectional Encoder Representations from Transformers) was able to achieve an F1 score of 0.757 for Task A (Binary Classification) and an F1 score of 0.449 for Task B (Multi-class Classification), retrospectively obtaining 4th and 9th ranks, in the two tasks respectively.