Using Generative AI (GenAI) tools in education presents both opportunities and challenges to the traditional teaching methods and students' learning experience and outcomes, particularly in technical and programming courses. This experience report evaluates the impact of GenAI tools, specifically ChatGPT and GitHub CoPilot, in leveling the playing field for Information Systems students with varying technical backgrounds in an application design and development course. By integrating these tools into course labs and projects, this study aimed to determine whether they improve the success rates of less technically prepared and struggling students. Data were collected from five sessions of a semester-long course across two campuses, involving 162 students with five parallel sessions across two continents. The analysis of student performance metrics and surveys revealed that GenAI tools significantly helped students complete programming tasks. However, those who were less technically prepared and relied heavily on AI assistance struggled with more complex, transformative tasks, such as closed-book exams. These findings suggest that while GenAI tools can help close gaps in temporary programming skills, they are less effective - and may even exacerbate disparities - in fostering long-term deeper learning and developing transformative knowledge and critical thinking.

The overall goal of our Software Engineering Teamwork Assessment and Prediction (SETAP) project is to develop effective machine-learning-based methods for assessment and early prediction of student learning effectiveness in software engineering teamwork. Specifically, we use the Random Forest (RF) machine learning (ML) method to predict the effectiveness of software engineering teamwork learning based on data collected during student team project development. These data include over 100 objective and quantitative Team Activity Measures (TAM) obtained from monitoring and measuring activities of student teams during the creation of their final class project in our joint software engineering classes which ran concurrently at San Francisco State University (SFSU), Fulda University (Fulda) and Florida Atlantic University (FAU). In this paper we provide the first RF analysis results done at SFSU on our full data set covering four years of our joint SE classes. These data include 74 student teams with over 380 students, totaling over 30000 discrete data points. These data are grouped into 11 time intervals, each measuring important phases of project development during the class (e.g. early requirement gathering and design, development, testing and delivery). We briefly elaborate on the methods of data collection and describe the data itself. We then show prediction results of the RF analysis applied to this full data set. Results show that we are able to detect student teams who are bound to fail or need attention in early class time with good (about 70%) accuracy. Moreover, the variable importance analysis shows that the features (TAM measures) with high predictive power make intuitive sense, such as late delivery/late responses, time used to help each other, and surprisingly statistics on commit messages to the code repository, etc. In summary, we believe we demonstrate the viability of using ML on objective and quantitative team activity measures to predict student learning of software engineering teamwork, and point to easy-to-measure factors that can be used to guide educators and software engineering managers to implement early intervention for teams bound to fail. Details about the project and the complete ML training database are downloadable from the project web site.

Self-adaptive systems are fed with contextual information from the environments in which the systems operate,from within themselves, and from the users. Traditional self-adaptive systems research has focused on inputs of systems performance, resources, exception, and error recovery that drive systems' reaction to their environments. The intelligent ability ofthese self-adaptive systems is impoverished without knowledge ofa user's covert attention (thoughts, emotions, feelings). As a result, it is difficult to build effective systems that anticipate and react to users' needs as projected by covert behavior. This paperpresents the preliminary research results on capturing users'intention through neural input, and in reaction, commanding actions from software systems (e.g., load an application) based on human intention. Further, systems can self-adapt and refine their behaviors driven by such human covert behavior. The long-term research goal is to incorporate and synergize human neural input.Thus establishing software systems with a self-adaptive capability to "feel" and "anticipate" users intentions and put the human in the loop.

Effective teaching of teamwork skills in local and globally distributed Software Engineering (SE) teams is recognized as an important part of the education of current and future software engineers. Effective methods for assessment and early prediction of learning effectiveness in SE teamwork are not only a critical part of teaching but also of value in industrial training and project management. This paper presents a novel analytical approach to the assessment and, most importantly, the prediction of learning outcomes in SE teamwork based on data from our joint software engineering class concurrently taught at San Francisco State University (SFSU), Florida Atlantic University (FAU) and Fulda University, Germany (Fulda). Our approach focuses on assessment and prediction of SE teamwork in terms of ability of student teams to apply best SE processes and develop SE products. It differs from existing work in the following aspects: a) it develops and uses only objective and quantitative measures of team activity from multiple sources, such as statistics of student time use, software engineering tool use, and instructor observations; b) it leverages powerful machine learning (ML) techniques applied to team activity measurements to identify quantitative and objective factors which can assess and predict learning of software engineering teamwork skills at the team level. In this paper we provide the following contributions: a) we present in detail for the first time the full team activity measurement data set we developed, consisting of over 40 objective and quantitative measures extracted from student teams working on class projects; b) we present a ML framework which applies the Random Forest (RF) algorithm to the team activity measurements and team outcomes, focusing on predicting teams that are likely to fail; c) we describe in detail our now fully implemented and operational data processing pipeline, consisting of data collection methods from multiple sources, ML training database creation, and ML analysis subsystems; and finally d) we present very preliminary results of ML analysis results based on the data from our joint software engineering classes in Fall 2012, and Spring 2013, with the data from 17 student teams. While our ML training database is currently small, it continuously grows. Our preliminary results, verified with two independent accuracy measures, show that RF is able to predict SE Process and SE Product team performance in intuitively explainable manner.

Modern software systems have grown in complexity and expense, even while the cost for supporting hardware has decreased over time. Humans have a lot to do with why software is expensive, and they contribute to its cost in at least three significant areas: the maintenance and evolution of existing software, the run-time monitoring and configuration of executing software, and errors made during data entry and system configuration tasks. Software engineers seek to mitigate these costs by minimizing or removing expensive human participation in these areas where possible by adopting software and hardware approaches aimed at doing so. In this paper, we describe a commercial software engineering project where code reuse, service-oriented architecture, and self-autonomic approaches were employed to extend the legacy enterprise system of a multi-channel vendor of musical equipment. In adopting these approaches, the developers were able to produce a highly-automated extension to an existing system that increased the number of orders places by customers, extending the business value of that system.

Given the time limit, software engineering courses in universities can only emphasize a particular development approach or method; therefore, it is challenging to prepare graduates to face the diverse range of approaches and methods used by industry. One of the issues software engineering education faces is the lack of a framework to understand and compare the similarities and differences among diverse practices used by different companies versus what students learn at school. Software Engineering Methods and Theory (SEMAT) is a new international initiative that bridges the gap among industry, education and research. SEMAT latest result is the submission to OMG standard, namely the Essence - Kernel and Language for Software Engineering. Instead of teaching diverse range of software engineering development methods, Essence provides a novel way of thinking of the different software development methods and approaches. This paper discusses the value of Essence to software engineering education and the preliminary feedback from university professors and lecturers.

One of the challenges in effective software engineering (SE) education is the lack of objective assessment methods of how well student teams learn the critically needed teamwork practices, defined as the ability: (i) to learn and effectively apply SE processes in a teamwork setting, and (ii) to work as a team to develop satisfactory software (SW) products. In addition, there are no effective methods for predicting learning effectiveness in order to enable early intervention in the classroom. Most of the current approaches to assess achievement of SE teamwork skills rely solely on qualitative and subjective data taken as surveys at the end of the class and analyzed only with very rudimentary data analysis. In this paper we present a novel approach to address the assessment and prediction of student learning of teamwork effectiveness in software engineering education based on: a) extracting only objective and quantitative student team activity data during their team class project; b) pairing these data with related independent observations and grading of student team effectiveness in SE process and SE product components in order to create “training database” and c) applying a machine learning (ML) approach, namely random forest classification (RF), to the above training database in order to create ML models, ranked factors and rules that can both explain (e.g. assess) as well as provide prediction of the student teamwork effectiveness. These student team activity data are being collected in joint and already established (since 2006) SE classes at San Francisco State University (SFSU), Florida Atlantic University (FAU) and Fulda University, Germany (Fulda), from approximately 80 students each year, working in about 15 teams, both local and global (with students from multiple schools).

The new software engineering initiative, Semat, is in the process of developing a kernel for software engineering that stands on a solid theoretical basis. So far, it has suggested a set of kernel elements for software engineering and basic language constructs for defining the elements and their usage. This paper describes a session during which Semat results and status will be presented. The presentation will be followed by a discussion panel.

Teaching academic courses to students with disabilities is a challenging task, particularly for academics who are presented with the teaching requirements and needs that this implies, for the first time. Courses in the field of engineering and computer science, by requiring a lot of handson practices and teamwork, further exacerbate the situation as how to provide an effective learning experience for these disabled students. This situation requires a higher-level commitment than normal, from both the teachers and students. This paper presents the experience gained from teaching courses that involved hearing-impaired students of an undergraduate software engineering and a programming language course in two different universities. Some of the challenges faced by both instructors and the students are identified and some possible solutions are described.

Abstract Web services are becoming increasingly important to many businesses, especially as an enabling technology for systems that adopt a service‐oriented architecture approach to their development. However, testing Web services poses significant challenges. This paper describes the design and use of WSDL‐Test, a tool designed specifically for this purpose. A key feature of WSDL‐Test is its ability to simulate the actual usage of Web services in a controlled environment. This enables WSDL‐Test to generate requests and validate responses in a rapid and reliable manner. To illustrate the use of WSDL‐Test, the paper also discusses our experience using the tool on a real‐world online eGoverment application. Copyright © 2007 John Wiley & Sons, Ltd.

This paper reports on the activities and results from the 2nd International Workshop on Software Engineering Course Projects (SWECP 2005), which was held on October 18, 2005 in Toronto, Canada. Creating software engineering course projects for undergraduate students is a challenging task. The instructor must carefully balance the conflicting goals of academic rigor and industrial relevance. Some of the fundamental characteristics of software engineering projects (e.g., team-based, large-scale, long-lived) are difficult to realize within the constraints of a university course in a single semester. This is particularly true when dealing with young students who may lack the real-world experience needed to appreciate some of the more subtle aspects of software engineering. This workshop explored how educators and industry can work together to develop a more rewarding educational experience for all stakeholders involved. Several key themes emerged from the workshop, including the importance of forming teams that are fair and balanced, the challenges in selecting a project that engages the students and meets the goals of the course, and the need for knowledge transfer amongst instructors.

This paper reports on a case study of redesigning a legacy application for the Web using the Ubiquitous Web Applications Design Framework with an extended version of its Transaction Design Model (UWAT+). Web application design methodologies hold the promise of engineering high-quality and long-lived Web systems and rich Internet applications. However, many such techniques focus solely on green-field development, and do not properly address the situation of leveraging the value locked in legacy systems. The redesign process supported by UWAT+ holistically blends design recovery technologies for capturing the know-how embedded in the legacy application with forward design methods particularly well suited for Web-based systems. The case study highlights some of the benefits of using UWAT+ in this context, as well as identifying possible areas for improvement in the redesign process and opportunities for tool automation to support it.

Abstract This paper provides a balanced perspective of the business value and technical challenges of adopting Web services. Technology adoption is a continual challenge for both tool developers and enterprise users. Web services are a prime example of an emerging technology that is fraught with adoption issues. Part of the problem is separating marketing hype from business reality. Web services are network‐accessible interfaces to application functionality. They are built using Internet technologies such as XML and standard protocols such as SOAP. The adoption issues related to Web services are complex and multifaceted. For example, determining whether this technology is a fundamental advance, rather than something old under a new name, requires technical depth, business acumen, and considerable historical knowledge of past developments. A sample problem from the health care industry is used to illustrate some of the adoption issues. Copyright © 2004 John Wiley & Sons, Ltd.

This paper describes an evaluation of the reverse engineering capabilities of three Web tools for understanding site content and structure. The evaluation is based on partitioning Web sites into three classes (static, interactive, and dynamic), and is structured using an existing reverse engineering environment framework (REEF). This case study also represents an initial evaluation of the applicability of the REEF in the related but qualitatively different domain of Web sites. The case study highlights several shortcomings of current Web tools in the context of aiding understanding to support evolution. For example, most Web tools are geared towards new page design and development, not to understanding detailed page content or overall site structure. The evaluation also identified some aspects of the REEF that might benefit from refinement to better reflect Web tool capabilities that support common evolution tasks. For example, Web server log file analysis as a specialized form of data gathering and subsequence information presentation.