Predicting Line-Level Defects by Capturing Code Contexts with Hierarchical Transformers

Software defects consume 40% of the total budget in software development and cost the global economy billions of dollars every year. Unfortunately, despite the use of many software quality assurance (SQA) practices in software development (e.g., code review, continuous integration), defects may still exist in the official release of a software product. Therefore, prioritizing SQA efforts for the vulnerable areas of the codebase is essential to ensure the high quality of a software release. Predicting software defects at the line level could help prioritize the SQA effort but is a highly challenging task given that only ~3% of lines of a codebase could be defective. Existing works on line-level defect prediction often fall short and cannot fully leverage the line-level defect information. In this paper, we propose Bugsplorer, a novel deep-learning technique for line-level defect prediction. It leverages a hierarchical structure of transformer models to represent two types of code elements: code tokens and code lines. Unlike the existing techniques that are optimized for file-level defect prediction, Bugsplorer is optimized for a line-level defect prediction objective. Our evaluation with five performance metrics shows that Bugsplorer has a promising capability of predicting defective lines with 26-72% better accuracy than that of the state-of-the-art technique. It can rank the first 20% defective lines within the top 1-3% suspicious lines. Thus, Bugsplorer has the potential to significantly reduce SQA costs by ranking defective lines higher

Developing a Meta-Model for Serious Games in Higher Education

This short paper presents a preliminary meta-model for educational games. A meta-model facilitates the development of high-quality, engaging, educational games because it explicitly ties knowledge requirements, transferable skills and course outcomes to game production. Our meta-model is designed to be transferable across curricula, as it modularizes domain specific bodies of knowledge (BOK), a learning taxonomy (e.g., Bloom\u27s), and skill based challenges. The model situates learning opportunities in a plotline wherein the student-player advances by succeeding against non-player adversaries. Knowledge-based challenges framed by a learning taxonomy develop the transferable skills required by international accreditation standards and provide feedback to both the player and the faculty member. Situating assessment challenges in an immersive game environment makes them more engaging and imaginative than typical on-line tests or assignments. Here, we present our meta-model tailored for educational game development in software engineering education

ASSESSMENT IN DEVELOPMENT COMPUTER-AIDED INSTRUCTION

Assessment in development computer-aided instruction (CAI) is not only done on the final products, but the assessment also takes place during the development process. Similar to assessments conducted by experts (Expert Judgment), the assessment also carried out by the user to individual persons (one to one), a small group, an expanded group, and real users (in dissemination process). In developing CAI, there are several aspects to be assessed such as programming, learning design, contents, and also its visual aspect. Moreover, there are several indicators that should be included in developing CAI which are: a. Software Engineering/Programming: (1). Effectiveness and Efficiency in the development and use of instructional media; (2). Reliable; (3).Maintainable (can easily be maintained and managed); (4). Usability (easy to use and simple in operation); (5).The accuracy in selection of the type of application / software / tool for development; (6) Compatibility (learning media can be installed / run on existing hardware and software); (7). Packaging of integrated instructional media and easy in execution; (8). Completeness of instructional media program documentation which consist of: installation manual (clear, brief, and complete); troubleshooting (clear, structured and anticipated); program design (clear and complete in describing program workflow); (9) Reusable (part or all program learning media can be reused to develop other learning media). b. Aspects of Learning Design and Content Items: (1) Clarity of learning objectives (formulation, realistic); (2) The relevance of the learning objectives with SK / KD / curriculum; (3) The scope and depth of learning objectives; (4) The accuracy of the use of learning strategies; (5) Interactivity; (6) Provision of motivation to learn; (7) Contextuality and actuality; (8) The completeness and the quality of learning support materials; (9) Compliance with the aim of learning materials; (10) The depth of the material; (11) Ease to be understood; (12) The systematic, continuous, clear logic flow; (13) The clarity of description, discussion, examples, simulations, exercises; (14) Consistency of evaluation with the aim of learning; (15) The accuracy and provision of evaluation tools; (16) The provision of feedback on the evaluation results. c. Aspects of Visual Communication / Display: (1) Communicative; according to the message and can be received / in line with the expected target; (2) Creative ideas pouring in the following idea; (3) Simple and attractive; (4) Audio (narration, sound effects, back sound, music; (5) Visual (layout design, typography, colour); (6) Media movement (animation, movie); (7) Interactive Layout (navigation icons). Keywords: Assessment, Development, CAI, Programming, Design, Visua

A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems

[EN] The upcoming avenue of IoT, with its massive generated data, makes it really hard to train centralized systems with machine learning in real time. This problem can be addressed with learning-based edge computing systems where the learning is performed in a distributed way on the nodes. In particular, this work focuses on developing multi-agent systems for implementing learning-based edge computing systems. The diversity of methodologies in agent-oriented software engineering reflects the complexity of developing multi-agent systems. The division of the development processes into method fragments facilitates the application of agent-oriented methodologies and their study. In this line of research, this work proposes a database for implementing a repository of method fragments considering the development of learning-based edge computing systems and the information recommended by the FIPA technical committee. This repository makes method fragments available from different methodologies, and computerizes certain metrics and queries over the existing method fragments. This work compares the performance of several combinations of dimensionality reduction methods and machine learning techniques (i.e., support vector regression, k-nearest neighbors, and multi-layer perceptron neural networks) in a simulator of a learning-based edge computing system for estimating profits and customers.The authors acknowledge PSU Smart Systems Engineering Lab, project "Utilisation of IoT and sensors in smart cities for improving quality of life of impaired people" (ref. 52-2020), CYTED (ref. 518RT0558), and the Spanish Council of Science, Innovation and Universities (TIN2017-88327-R).García-Magariño, I.; Nasralla, MM.; Lloret, J. (2021). A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems. IEEE Network. 35(1):156-162. https://doi.org/10.1109/MNET.011.2000296S15616235

Exploring assessment of on-line collaboration in distance education : an action research study

Computer Supported Collaborative Learning (CSCL) environments offer the perfect opportunity to explore self and peer assessment practices. Through an Action Research approach, this study explores the use of self and peer assessment within an on-line learning context. The process took place over a twelve-month period and involved students registered in the Winter and Fall, 2000 semester sections of the same undergraduate course. The course, Technology for Educational Change , is offered by the department of Education at Concordia University and is delivered completely at a distance using FirstClass ® software. Findings suggest that learners do require support in developing collaborative skills for on-line group work. The quality of learners' collaborative interaction was directly related to the quality of products produced by groups. Results also indicate the need for educators to find ways to support the development of learners' evaluative skills. In conclusion, recommendations for the orchestration of self and peer assessment practices to meet these instructional goals are offered

AI-assisted anomaly detection from log data

As the production of software continues to increase, the volume of log data being generated is also on the rise. This surge in data has made it impractical for human operators to manually review each log line produced by software systems. This necessity has led to the development of automatic anomaly detection methods. Automatic anomaly detection methods would allow system operators to respond to incidents more quickly and improve the quality of the software. In the past, anomaly detection from log data relied heavily on predefined rules. However, with the complexity of modern software systems, finding experts for every system component to write these rules has become difficult. Additionally, it is very labor-intensive to implement these rules. This has spurred interest in unsupervised anomaly detection methods. The purpose of this thesis is to research which kind of methods can be used for automatic anomaly detection, what is required to use them in a production system, and how well deep learning-based methods would work with log data produced by hundreds of embedded devices. The thesis begins with a literature review to explore the various methods used for anomaly detection from log data. It then outlines the required infrastructure for efficient anomaly detection and concludes by testing the DeepLog Deep Learning method on real log data from a production system. The key findings suggest that the DeepLog model performs effectively for anomaly detection when trained in an unsupervised fashion. However, it is essential to ensure that anomalous samples do not dominate the training data. This can be achieved either by completely excluding them from the training set or by ensuring that no single anomalous sample overwhelms the entire dataset, which could lead to overfitting. Moreover, the training dataset can be continuously refined by eliminating recognized anomalous sequences and subsequently retraining the model

Game Edukasi Pengenalan Jenis Alat Transportasi Untuk Anak Usia 4-5 Tahun Di TK Al-Huda Colomadu Karanganyar

In today's life, technology is continuously advancing, but the educational learning methods remain conventional, relying on books and teaching aids. This situation has an impact on improving the quality of students' learning. Conventional learning methods at an early age can make children quickly bored, resulting in difficulties in absorbing the material. The purpose of this research is to create an Android-based educational game for introducing transportation tools to children aged 4-5 years at TK AL-HUDA Colomadu Karanganyar, aiming to enhance the quality of early childhood learning. The research method utilizes the Software Construct 2 and follows the Game Development Life Cycle (GDLC) model waterfall design method. Testing of the game is conducted using two methods, namely Blackbox and System Usability Scale (SUS). The obtained results are in line with expectations, where all output commands correspond to input commands. The System Usability Scale (SUS) yields an average score of 83.92, indicating that the game meets the required criteria and fulfills the needs of students and teachers at TK Al-Huda Colomadu

Development of an E-Learning Module For Global Navigation Satellite SystemsTraining

The Global Positioning System (GPS) has been operational since the early 1990’s. The system is constantly being upgraded while the Russian GLONASS and the European GALILEO systems will complement GPS in the next few years. Generically, these satellite-based positioning systems are referred to as Global Navigation Satellite Systems (GNSS). Previously, a part-time evening course in GPS was run at the Technological University Dublin (DIT) by the Department of Spatial Information Sciences (DSIS). This is now being replaced by two e-learning modules in GNSS designed for distance-based, on-line delivery. The first module covers GNSS for navigation and Geographical Information Systems (GIS) applications and the second module will cover GNSS for high-accuracy applications such as surveying and geophysics. This paper describes the development of the first GNSS module. The challenge for the course designers was to develop a module that, in the context of the spatial information industry, maximized the advantages of e-learning while addressing identified issues and maintaining a sound pedagogical approach. Potential users were identified as those engaged in continuous personal/professional development (CPD), organizations providing in-company training and academic institutions providing undergraduate and postgraduate modules in GNSS. An individual taking the module could, therefore, be based at home, in an office, in a classroom or in a remote work location. The advantages were identified as convenience, flexibility, facilitation of communication, tailoring and a varied learning experience. The issues were identified as no “hands-on” experience with GNSS field equipment and observing procedures, the difficulty of presenting complex software, learner isolation and the technical problems of delivering large files. To address these issues, as far as possible, a variety of presentation, delivery, contact and assessment approaches is being used. Content is presented in various formats including text, 2D and 3D graphics, animations with animation control, use of proprietary GNSS software with sample data and video with voiceover. Interaction between the parties is facilitated by email, discussion board and desktop videoconferencing. Self-assessment is included as a series of self-tests throughout the content in the form of multiple choice questions (MCQs) while written assignments are required at the end of each section, or theme, within the module. WebCT® is used to provide a consistent e-learning platform and environment. Quality assurance is provided for by questionnaires during the module and a feedback report by each participant after completion