Qualitative Research and Computer Analysis: New Challenges and Opportunities

The use of computers for Qualitative Data Analysis (QDA) in qualitative research has been growing rapidly in the last decade. QDA programs are software packages developed explicitly for the purpose of analyzing qualitative data. A range of different kinds of program is available for the handling and analysis of qualitative data, such as Atlas/ti, HyperRESEARCH, and NUD*IST. With the development of new technologies, the QDA software has advanced from the efficient code-and-retrieve ability to the development of sophisticated organizing system or conceptual tool for data analysis as well as information management. This presentation is an initial discussion on the impact of database and web technology on QDA. The following issues will be discussed: why or how QDA methods are different from the database approach as means of managing and exploring unstructured data; what data mining and web search tools offer to qualitative research; what QDA software should do to support web-based research; and what methodological and technical problems are posed for web-based QDA by the web itself.published_or_final_versionCentre for Information Technology in Education, University of Hong Kon

Dynamic data flow testing

Data flow testing is a particular form of testing that identifies data flow relations as test objectives. Data flow testing has recently attracted new interest in the context of testing object oriented systems, since data flow information is well suited to capture relations among the object states, and can thus provide useful information for testing method interactions. Unfortunately, classic data flow testing, which is based on static analysis of the source code, fails to identify many important data flow relations due to the dynamic nature of object oriented systems. This thesis presents Dynamic Data Flow Testing, a technique which rethinks data flow testing to suit the testing of modern object oriented software. Dynamic Data Flow Testing stems from empirical evidence that we collect on the limits of classic data flow testing techniques. We investigate such limits by means of Dynamic Data Flow Analysis, a dynamic implementation of data flow analysis that computes sound data flow information on program traces. We compare data flow information collected with static analysis of the code with information observed dynamically on execution traces, and empirically observe that the data flow information computed with classic analysis of the source code misses a significant part of information that corresponds to relevant behaviors that shall be tested. In view of these results, we propose Dynamic Data Flow Testing. The technique promotes the synergies between dynamic analysis, static reasoning and test case generation for automatically extending a test suite with test cases that execute the complex state based interactions between objects. Dynamic Data Flow Testing computes precise data flow information of the program with Dynamic Data Flow Analysis, processes the dynamic information to infer new test objectives, which Dynamic Data Flow Testing uses to generate new test cases. The test cases generated by Dynamic Data Flow Testing exercise relevant behaviors that are otherwise missed by both the original test suite and test suites that satisfy classic data flow criteria

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Large Language Models for Programming Industrial Control Systems and Mitigating Real-World Software Vulnerabilities

This manuscript is comprised of two sections — automated code generation for Programmable Logic Controllers and vulnerability repair for Common Vulnerabilities & Exposures (CVEs) with Large Language Models (LLMs). The application of LLMs to Industrial Control Systems (ICS) is a relatively unexplored area. State-of-the-art LLMs such as GPT-4 and Code Llama fail to produce valid programs for ICS operated by Programmable Logic Controllers (PLCs). As a result, there is abundant potential to incorporate the use of Large Language Models into the PLC programming process to achieve end-to-end automation of common ICS tasks. We propose LLM4PLC, a user-guided iterative pipeline leveraging user feedback and external verification tools — including grammar checkers, compilers, SMV verifiers — as well as Parameter-Efficient Fine-Tuning and Prompt Engineering, to guide the LLM's generation. We run a complete test suite on GPT-3.5, GPT-4, Code Llama-7B, a fine-tuned Code Llama-7B model, Code Llama-34B, and a fine-tuned Code Llama-34B model. Ultimately, we demonstrate that the LLM4PLC pipeline improves the generation success rate from 47% to 72%, and the Survey-of-Experts code quality from 2.25/10 to 7.75/10. Software vulnerabilities continue to be ubiquitous, even in the era of AI-powered code assistants, advanced static analysis tools, and the adoption of extensive testing frameworks. It has become apparent that we must not simply prevent these bugs, but also eliminate them in a quick, efficient manner. Yet, human code intervention is slow, costly, and can often lead to further security vulnerabilities, especially in legacy codebases. The advent of highly advanced Large Language Models (LLM) has opened up the possibility for many software defects to be patched automatically. We propose LLM4CVE — an LLM-based iterative pipeline that robustly fixes vulnerable functions with high accuracy. We examine our pipeline with State-of-the-Art LLMs, such as GPT-3.5, GPT-4o, Llama 3 8B, and Llama 3 70B, along with fine-tuned variants of selected models. We achieve an increase in ground-truth code similarity of 20% with Llama 3 80B

Ada (trademark) projects at NASA. Runtime environment issues and recommendations

Ada practitioners should use this document to discuss and establish common short term requirements for Ada runtime environments. The major current Ada runtime environment issues are identified through the analysis of some of the Ada efforts at NASA and other research centers. The runtime environment characteristics of major compilers are compared while alternate runtime implementations are reviewed. Modifications and extensions to the Ada Language Reference Manual to address some of these runtime issues are proposed. Three classes of projects focusing on the most critical runtime features of Ada are recommended, including a range of immediately feasible full scale Ada development projects. Also, a list of runtime features and procurement issues is proposed for consideration by the vendors, contractors and the government

Using Emotional Intelligence in Personalized Adaptation

Damjanovic, V. & Kravcik, M. (2007). Using Emotional Intelligence in Personalized Adaptation. In V. Sugumaran (Ed.), Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications (pp. 1716-1742). IGI Publishing.The process of training and learning in Web-based and ubiquitous environments brings a new sense of adaptation. With the evelopment of more sophisticated environments, the need for them to take into account the user’s traits, as well as the user’s devices on which the training is executed, has become an important issue in the domain of building novel training and learning environments. This chapter introduces an approach to the realization of personalized adaptation. According to the fact that we are dealing with the stereotypes of e-learners, having in mind emotional intelligence concepts to help in adaptation to the e-learners real needs and known preferences, we have called this system eQ. It stands for the using of the emotional intelligence concepts on the Web.PROLEARN - Network of Excellence in Professional Learnin