Report on the 2nd Software Engineering Education Workshop (SEED 2019) co-located with APSEC 2019

SEED 2019, The 2nd International workshop on Software Engineering Education (co-located with Asia-Pacific Software Engineering Conference – APSEC 2019, 2-4 December, at Putrajaya, Malaysia with a theme 'Engineering Impactful Software for the Society towards IR4.0'), aims to bring together Software Engineering (SE) educators and practitioners into a dialogue to build a shared understanding of Software Engineering curriculum topics and specific issues in teaching and learning of Software Engineering with respect to the emerging topics of Artificial Intelligence, Cloud Computing, and Internet of Things (IoT) and corresponding Industry practices. SEED 2019 invited Position Papers (maximum 6 pages long) in the area of Software Engineering. The workshop accepted 5 papers and consisted of keynote talk as well as group discussion in addition to the position paper presentations

SMILE/ MARVEL: Two Approaches to Knowledge-Based Programming Environments

This technical report consists of three related papers in the area of intelligent assistance for software development and maintenance. Intelligent Assistance without Artificial Intelligence describes SMILE, a software engineering environment that assists teams of programmers without using AI technology. An Architecture for Intelligent Assistance in Software Development presents an AI approach to generalizing the capabilities of SMILE. Granularity Issues in a Knowledge-Based Programming Environment briefly describes MARVEL, an intelligent assistant based on this AI approach, and compares it to SMILE

Integrated design optimization research and development in an industrial environment

An overview is given of a design optimization project that is in progress at the GE Research and Development Center for the past few years. The objective of this project is to develop a methodology and a software system for design automation and optimization of structural/mechanical components and systems. The effort focuses on research and development issues and also on optimization applications that can be related to real-life industrial design problems. The overall technical approach is based on integration of numerical optimization techniques, finite element methods, CAE and software engineering, and artificial intelligence/expert systems (AI/ES) concepts. The role of each of these engineering technologies in the development of a unified design methodology is illustrated. A software system DESIGN-OPT has been developed for both size and shape optimization of structural components subjected to static as well as dynamic loadings. By integrating this software with an automatic mesh generator, a geometric modeler and an attribute specification computer code, a software module SHAPE-OPT has been developed for shape optimization. Details of these software packages together with their applications to some 2- and 3-dimensional design problems are described

Intelligent Embedded Software: New Perspectives and Challenges

Intelligent embedded systems (IES) represent a novel and promising generation of embedded systems (ES). IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence (AI)-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-repairing. Despite the widespread of such systems, some design challenging issues are arising. Designing a resource-constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded system researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality

COMPUTERIZED REASONING AND ITS APPLICATION IN DIFFERENT AREAS

Later on, intelligent machines will supplant or improve human abilities in numerous ranges. Manmade brainpower is the insight displayed by machines or programming. It is the subfield of software engineering. Counterfeit consciousness is turning into a prevalent field in software engineering as it has improved the human life in numerous regions. Counterfeit consciousness over the most recent two decades has extraordinarily enhanced execution of the assembling and administration frameworks. Concentrate in the territory of manmade brainpower has offered ascend to the quickly developing innovation known as master framework. Application zones of Artificial Intelligence is huge affecting different fields of life as master framework is broadly utilized nowadays to take care of the perplexing issues in different ranges as science, building, business, solution, climate estimating. The territories utilizing the innovation of Artificial Intelligence have seen an expansion in the quality and proficiency. This paper gives an outline of this innovation and the application regions of this innovation. This paper will likewise investigate the present utilization of Artificial Intelligence advances in the PSS configuration to clammy the power framework motions caused by interferences, in Network Intrusion for shielding PC and correspondence systems from gatecrashers, in the therapeutic region prescription, to enhance healing facility inpatient mind, for restorative picture arrangement, in the bookkeeping databases to alleviate its issues and in the PC recreations

A Rapid Scoping Review on Academic Integrity and Algorithmic Writing Technologies

This presentation provides insight into the development and findings of a rapid scoping review centred on the intersections of academic integrity and artificial intelligence, with particular attention to algorithmic writing technologies (e.g., ChatGPT) involving faculty, students, teaching assistants, academic student support staff, and educational developers in higher education contexts. This rapid scoping review was developed by a transdisciplinary team including Communication studies, Education, Engineering, and English, and followed Joanna Brigg Institute’s (JBI) updated manual for scoping reviews and the Preferred Reporting Items for Systematic reviews Meta-Analysis (PRISMA) reporting standards. JBI provides a high-quality, trusted framework for conducting these kinds of studies. This inquiry’s study design includes qualitative, quantitative, mixed methods, theoretical and opinion studies; additionally, this inquiry did not restrict studies by geographic location and focused on sources written in English. This review’s studies involved faculty, students, teaching assistants, academic support staff, and educational developers in higher education. It also included studies about artificial intelligence in the context of academic integrity, focusing on artificial intelligence tools that assist text generation and writing developed in Tertiary type A and B postsecondary education. Studies excluded from this review were related to primary and secondary education contexts, did not address the ethical implications of artificial intelligence, and focused on text plagiarism software. The protocol of this rapid review was published in the Canadian Perspectives on Academic Integrity Journal. Its implementation helped this team identify various ethical implications signalled by scholars between 2007 and 2022. Considering the expansive emergence of these technologies and the multiple positionings derived from these new and unprecedented encounters with such technology, we believe that the implications identified in this rapid scoping review are particularly relevant to inform academic staff, administration, students, and academic integrity researchers’ ethical decision-making and practices when teaching, learning, designing, and implementing assessments, and doing research. The findings of this rapid scoping review encompass nuanced perspectives concerning the ethical and unethical uses of these emerging technologies and insights into equity, diversity, and inclusion issues

Knowledge capture and reuse through expert’s activity monitoring in engineering design

This paper deals with artificial intelligence driven product engineering support. Many software systems are available to support the product lifecycle, especially during product design, such as CAD, PDM, CAE, SDM, etc. Most product development process is performed using these systems, which through their rich user interfaces allow skilled professionals to express their expertise and knowledge using the tools and functions the software is willing to provide them. At the end of the day, the result of their work is a model, built through a user interface, and stored in a repository. The goal of our research is to reverse engi-neer the user’s knowledge by analysing his/her actions with the software system, based on the assumption that the process will itself be meta-knowledge driven and that we will focus on engineering software which provide semantically rich user interfaces. The aim of this paper is to investigate the idea of building reusa-ble expert knowledge from actions on engineering software user interfaces. It first outlines existing works from different fields and identifies remaining issues. It then suggest an approach to address these issues and put together an operational system

A First Look at Fairness of Machine Learning Based Code Reviewer Recommendation

The fairness of machine learning (ML) approaches is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML models in the software engineering (SE) domain has not been well explored yet. As a result, many ML-powered software systems, particularly those utilized in the software engineering community, continue to be prone to fairness issues. Taking one of the typical SE tasks, i.e., code reviewer recommendation, as a subject, this paper conducts the first study toward investigating the issue of fairness of ML applications in the SE domain. Our empirical study demonstrates that current state-of-the-art ML-based code reviewer recommendation techniques exhibit unfairness and discriminating behaviors. Specifically, male reviewers get on average 7.25% more recommendations than female code reviewers compared to their distribution in the reviewer set. This paper also discusses the reasons why the studied ML-based code reviewer recommendation systems are unfair and provides solutions to mitigate the unfairness. Our study further indicates that the existing mitigation methods can enhance fairness by 100% in projects with a similar distribution of protected and privileged groups, but their effectiveness in improving fairness on imbalanced or skewed data is limited. Eventually, we suggest a solution to overcome the drawbacks of existing mitigation techniques and tackle bias in datasets that are imbalanced or skewed