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The Use of GenAI in Graph Based Unit Testing
No full textSoftware testing verifies that the software is free of defects and meets its requirements. This process includes various levels, one of which is unit testing, where developers create test cases alongside their regular code, and use frameworks, such as JUnit for Java, to enable a frequent automated execution of these test cases. However, designing test cases remains a significant challenge. Graph-based testing offers a solution by representing units in the source code as graphs, with nodes representing basic code blocks and edges representing transitions or interactions between these nodes. Additionally, modern Generative AI (GenAI) models, including ChatGPT, Gemini, and Copilot, present new opportunities for enhancing the software testing process. This paper investigates the potential of using GenAI models to automate and improve unit testing, particularly through graph-based methods. Experiments are designed to evaluate these models, assessing their ability to reduce manual effort while improving test coverage, efficiency, and code quality. The results reveal that GenAI models can streamline test generation and execution, but their effectiveness heavily relies on prompt quality and they lack an inherent understanding of program logic. In contrast, traditional graph-based unit testing ensures comprehensive coverage through systematic exploration of control flow paths but is resource-intensive. AQ1 Therefore, this paper recommends a hybrid approach that combines the automation capabilities of GenAI with the rigor of traditional methods to achieve robust and efficient software testing
The Use of Call Graphs and Deep Learning to Improve Software Testing
No full textSoftware testing is a critical part of software development, it is essential for preventing failures and enhancing software quality attributes. However, the testing process can be costly and time-consuming, often involving a large number of test cases. Over time, the accumulation of redundant and overlapping test cases can complicate and lengthen the testing time. To address these challenges, this paper utilizes graph similarity and deep learning techniques to optimize test suites. It uses call graphs from test cases to identify redundant and similar test cases. A machine learning model is used to calculate and predict the similarity scores between these call graphs, helping to classify and prioritize the test cases. This helps rank test cases based on their similarity scores, with lower scores indicating higher priority due to their unique code coverage. This approach allows test engineers to focus on the most diverse set of test cases, ensuring comprehensive code coverage and efficient testing. By reducing the number of redundant test cases, this method aims to streamline the testing process, reduce costs, and maintain high software quality standards. Ultimately, this paper seeks to provide a systematic framework for test engineers to determine the optimal amount of testing needed to effectively meet the software quality objectives
Navigating difficult conversations and recognizing persons of concerns: Confrontation, de-escalation, and threat awareness
No full textA student angry about their grade, a peer not pulling their weight, a supervisor sharing criticism; all can trigger anxiety and stress. Human beings experience physiological responses to stressful encounters that inhibit our ability to communicate, problem solve and listen when it’s most important to do so. Targeted attacks, like active shooters incidents, are not spontaneous, sudden events which occur without warning. They are predictable and, consequently, preventable. Students, co-workers or others may exhibit risk factors or observable behaviors that would indicate they may be on the “pathway to violence.”
In this interactive presentation, participants will be introduced to methods to manage stress during a confrontation to remain intellectually competent to manage the encounter without succumbing to the instinctive visceral reactions that derail our rational responses. Several tools will be introduced for managing difficult conversations and confrontations. Participants will discover, through a self-assessment, their own personal conflict management style and understand how it affects their ability to collaborate toward reaching mutually positive outcomes.
Additionally, this program will provide a basic understanding of the behavioral evolution of an attacker and help participants to recognize and respond to potential signs or cues that may indicate an individual is in distress, in need of help, or may be planning violence, and what interventions might help prevent an attack.
Learning Objectives: Participants will gain insight into the natural physiological stress responses that affect cognitive capacity. Participants will learn skills to mitigate visceral responses to confrontation that inhibit problem solving. Participants will complete a self-assessment to determine their dominant conflict management style. Participants will learn and practice tools for effective de-escalation and confrontation. Participants will gain an understanding of basic threat assessment principles
Nueva metodología de análisis para determinar el delito ambiental de ecocidio por glifosato en cultivos permanentes de coca en Colombia
No full textEl crimen organizado transnacional ha sido nefasto para el territorio colombiano, en especial el asociado a la producción de coca en la última década, lo que ha generado impactos económicos y sociales debido al aumento del conflicto e inseguridad en las zonas de influencia del cultivo. Esto se debe a que en el 2023 se tenían 230 000 hectáreas sembradas, lo que representa el 20 % de la superficie del país, con un impacto ambiental no cuantificado en términos penales. Este requiere de acciones de entidades estatales, así como de cooperación internacional, para adaptar estrategias y poder judicializar estos delitos que afectan a generaciones de personas, con nuevas técnicas forenses. En el presente trabajo se aplicó una nueva metodología mediante el seguimiento de la actividad metabólica microbiana a lo largo del tiempo, usando EcoPlatos. Esta técnica novedosa para el país fue el resultado de la sinergia entre la Universidad de La Salle (Bogotá) y su aliada lasallista Lewis University, la cual se aplicó a tres muestras de suelo de reservas, parques industriales y campos agrícolas con más de quince años de aplicación del químico, bajo técnicas estándar. Se analizó el crecimiento celular usando EcoPlatos, lo que permitió establecer la utilización de sustratos de carbono por los microorganismos del suelo y evidenciar la pérdida definitiva de especies clave para las cadenas tróficas. Con esta prueba se podría tipificar el ecocidio, ya que es innegable lo que se ocasiona en la base del bioma y que se extiende a las otras especies en la cadena
Biz of Digital - Implementing Digital Systems for the Lewis University Archives and the Creation of an Institutional Repository
Full text linkTinkering in Clinical Practice: Exemplars from SLPs Working with Persons in Disordered States of Consciousness
No full textSpeech-language pathologists (SLPs) providing care for people living with disordered states of consciousness frequently navigate ambiguity and uncertainty. Using interview and video data we will show how SLPs clinically reason via tinkering or “thinking in action”. This undervalued and understudied clinical reasoning skill is a caring practice comprised of curiosity, experimentation, flexibility and adaptation to attend to a person’s needs. SLPs will have the opportunity to reflect and discuss their experiences with tinkering in a wide range of patient populations
