August 12-13, 2023, Virtual Conference
Manuel Ignacio Castillo López1, Ana Libia Eslava Cervantes2 and Gustavo de la Cruz Martínez3, 1, 3Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico – ORCID: 0000-0002-2307-5860, 2Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Mexico City, Mexico
Agile methods in undergraduate courses have been explored by various authors looking to close the gap between industry and professional profiles. We have structured an Android application development course based on a tailored agile process for development of educational software tools. This process is based on both Scrum and Extreme Programming in combination with User Experience (UX) and UserCentered Design (UCD) approaches. The course is executed in two phases: the first half of the course’s semester presents theory on agile and mobile applications development, the latter half is managed as a workshop where students develop for an actual client. The introduction of UX and UCD exploiting the close relationship with stakeholders expected from an agile process can enhance Quality in Use features. Since 2019 two of the projects have been extended in agreement between the client and students. Students, clients and users have found value in the generated products.
Agile development, User-Centered Design, User Experience, Software development, Undergraduate teaching
F. OUAZAR, M.C. BOUKALA, M.IOUALALEN, MOVEP, Computer Science Faculty, USTHB BP, 32 El-Alia, Algiers, ALGERIA
Despite the richness of the BPMN language and its advantages for the specification of business processes, it remains a semi-formal language that does not allow rigorous verification of the specifications produced with it, and does not offer any methodological support to cover the verification phase. Therefore, several works have been proposed with the aim of describing the semantics of the BPMN language by a mathematical formalism. In this paper we address the issue of verifying BPMN models with an approach based on model-checking, where we focus on soundness, fairness, and safety properties. Thus by having a business process modeled by BPMN, a formal semantics for BPMN models based on Kripke structure will be provided for a formal verification of correctness. The properties are expressed with CTL (Computation Tree Logic) formulas. At the end, the model checker NuSMV is used for the verification of the formula.
Business process, model-checking, formal methods, temporal logic CTL, Kripke structure.
Hussain T Alsalem1, Faisal M Alotaibi2, Mohsen H Bamardouf3, Ibrahim A Abukhamseen4, Hussain A AlGallaf5, Yosef A Junaid6, 1, 3, 4, 5, 6College of Computer Sciences & Information Technology, Imam Abdulrahman Bin Faisal University, Kingdom of Saudi Arabia, 2Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
Laboratories in colleges are used to give lectures to students, but what about after working hours? Students can get many benefits from these labs after working hours. For example, students can study and do their homework after working hours. In this project, we have proposed a new technique to control the access of these laboratories. Our idea is to use an encrypted QR code with an OTP authentication that will be connected to LAN network to guarantee that each student enter can only use a single PC. Each student will have his own encrypted QR code that differs from other students. The OTP code will be sent to student’s university email. Our encrypted method used RSA encryption to encrypt the data inside each QR code to guarantee the confidentiality, integrity, non-repudiation, and authenticity of the date.
Security, QR Code, Access control, embedded systems, image detection, OTP.
Harvindran Chandrasekaran1, Tan Yu Xuan2, Tang Kok Mang3, 1Intel Technology Sdn. Bhd, Bayan Lepas, Malaysia, 2Intel Technology Sdn. Bhd, Bayan Lepas, Malaysia, 3Intel Technology Sdn. Bhd, Bayan Lepas, Malaysia
Intel software development ecosystem is complex, consisting of components produced in-house, third-party vendors and open-source community. Different tools and processes are being used to manage individual deliverables throughout the whole software product life cycle. Managing the release of such complex software solution is very competitive, especially when dealing with the dependencies of many moving pieces across different department which contribute to an array of products releases with different requirements and schedule. This paper describes Owl’s View, high performance and scalable personalized dashboard’s architecture which enables very fast data lookup from multiple sources; it also explains how productivity gains can be achieved through eliminating unnecessary waste and summarizing day to day top priority tasks in single dashboard; as well as revealing the impacts that Owl’s View brings to the organization, especially in productivity gains and Intel Software Quality compliance.
Actionable Insight, Interactive, Personalized dashboard, Self-service.
Aljia Bouzidi, Kais Haddar, FSEGS, Tunisia
In the software development lifecycle, business process models (BPMs) turn out to play an ever more pivotal role in the development and continued management of information systems (ISs). However, BPMs and IS models (ISMs) are traditionally expressed separately. This separation causes drift between them, impedes their interoperability, and thus builds up misaligned models. Traceability in software development proves its ability to link together related artifacts from different sources within a project (for examples, business modelling, requirements, design models), improves project outcomes by assisting designers and other stakeholders with common tasks such as impact analysis, etc. In this paper, we propose an improvement and an extension of an existing requirement traceability method in order to tackle the traceability between design, requirement and BPMs. In fact, the extension consists in adding the UML class diagram concepts structured according to the Model View Controller (MVC) design pattern to be traced with BPMN and UML use cases models in a single unified model. This method is based on the integration mechanism, acts at the model and the meta-model levels, and can be used to develop a new IS and/or to examine the misalignment of the existing ISMs and the BPMs after BPM/ISM evolution.
alignment;class diagram; MVC design pattern; BPMN model; use case diagram.
DILSHAN DE SILVA1, PIYUMIKA SAMARASEKARA2, SHALINIKA DE SILVA3, KETHANI ARIYARATHNA4, LANKA DE SILVA5, HASARA METHMINI6, SHANIKA WIJEWICKRAMA7, JANANI MADUSHIKA8, Department of Computer Science and Software Engineeing Sri Lanka Institute of Information Technology Malabe, Sri Lanka
The effectiveness of software testing frameworks on software quality is an important area in software engineering. This paper evaluates the effectiveness of popular software testing frameworks, namely Selenium, TestNG, Cucumber, JMeter, and Cypress, on different software qualities, including maintainability, security, code quality, reliability, performance, and usability. The study includes a literature review of previous studies on the effectiveness of these testing frameworks and an empirical study that compares the performance of these frameworks on the selected software qualities using a set of benchmark applications. The results show that the effectiveness of a software testing framework on a particular software quality depends on the specific requirements of the project. For example, Selenium and Cypress are effective for testing user interface functionality and usability, while JMeter is effective for load and stress testing. Thus, software developers and testers should carefully evaluate the different testing frameworks in relation to the software quality requirements of their project before selecting a testing framework. The study concludes that software testing frameworks play a crucial role in ensuring software quality, and selecting the appropriate testing framework is important for achieving the desired software quality goals.
Result Analysis, Software Quality, Effectiveness Evaluation, Software Frameworks, Evaluation Criteria.
Dilshan De Silva, Madusha Weerasooriya, Tharushi Ranaweera, Joshua Amarasinghe, Dasun Wadasinghe and Pawan Kaluarachchi, Department of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology Malabe, Sri Lanka
To make sure that an e-commerce site is functional and meets customer needs, software testing is a crucial part of the development process. Two popular methods used in software testing, especially for e-commerce sites, are unit testing and integration testing. In order to discover the best efficient testing strategy for ecommerce sitedevelopment, this study focuses on the unit and integration testing of new functionality for tracking orders placed by customers. This will focus on how each testing strategy contributes to the site's quality, dependability, and security aswell as the time and resources needed to carry out each form of testing. Additionally, the study will determine the best procedures for testing e-commerce websites and offer advice on how software developers can streamline their testing procedures. The findings of this study can help softwaredevelopers improve the overall quality of their e-commerce sites by illustrating the most efficient testing methodology fore-commerce site development.
Unit Testing, Integration Testing, Effectiveness.
Dilshan De Silva, Piyumika Samarasekara, Suresh Hemantha, Tharuka Wijesooriya, Nimesh Rathnayaka, Sumeera Rathnayaka, Department of Computer Science andSoftware Engineering Sri Lanka Institute of InformationTechnology, Malabe, Sri Lanka
The increasing popularity of mobile applications has led to a rise in the demand for effective software testing tools toensure their quality and reliability. With a plethora of automated testing tools available in the market, it has become essential to evaluate their effectiveness in testing mobile applications. The research topic aims to investigate the effectiveness of software testing tools for mobile applications by examining their strengths and limitations. The study will focus on popular automated testing tools such as Appium, Robotium, Selendroid, UI Automator, Calabash, and Espresso, among others. The research will begin by identifying the testing methodologies used for mobile application testing, including manual and automated testing. The study will alsoidentify the types of mobile applications tested, including gaming, social media, e-commerce, health and fitness, productivity, and education. Furthermore, the research will evaluate the effectivenessof each testing tool by examining specific factors such as test coverage, defect detection rate, ease of use, compatibility withmultiple platforms, and cost. The study will also identify any challenges faced by the testers while using these automated testing tools. Investigating the effectiveness of software testing tools for testing mobile applications is a crucial research topic that can help improve the quality and reliability of mobile applications. Theresearch will provide valuable insights into the strengths and limitations of existing testing tools and identify areas that require improvement to enhance the overall effectiveness of mobile application testing.
Software testing, Mobile applications, Testing methodologies, Testing tools, Effectiveness, Automated testing,Manual testing, Test coverage, Defect detection rate, Compatibility, cost, Challenges, Descriptive statistics, Inferential statistics, Correlation analysis, Research design, Data collection, Data analysis.
Dilshan De Silva, Poojani Gunathilake, Kavindi Kariyawasam, Chandima Medawela, Kasun Samarakoon, Dilshan Bandara, Department of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
This research aims to investigate the relationship between software metrics and software performance. Software metrics have been widely used to evaluate the quality and maintainability of software systems. However, their relationship with software performance has not been extensively studied. The study utilizes a quantitative research methodology, where software metrics are collected from a number of software projects and analyzed to determine their correlation with software performance. The performance metrics considered in this research include response time, throughput, and resource utilization. The results of the analysis show that there is a significant relationship between software metrics and software performance. Specifically, the study finds that certain software metrics, such as code complexity and code size, are strongly correlated with software performance, while others, such as coupling and cohesion, have a weaker correlation. These findings can help software developers and project managers to identify the key software metrics that impact software performance and prioritize their efforts accordingly.
software metrics, software performance, code complexity, scalability, reliability.
Ferdinand Alifo1 Mustapha Awinsongya Yakubu2 and Prof. Michael Asante3, 1 MIS/Computer Dep., Ministry of Local Government, Local Gov’t Service, Sekyere East District Ass., Ghana, 2 University of Cincinnati Ohio, USA, 3Department of Computer Science, Kwame Nkrumah University of Science and Technology, Ghana
This paper discusses Mobile Ad-hoc Networks (MANETs), which are self-organizing networks without infrastructure relying on wireless connections between mobile nodes. The open communication channels in MANETs make them vulnerable to attacks, compromise privacy, and reduce throughput. The paper aims to investigate the impact of wormhole attacks on MANETs, highlighting the significant security threat they pose to wireless networks despite the presence of authentication and confidentiality measures. This paper utilized ns-allinone-2.35-RC7 as a simulation environment to study the effects of introducing Wormhole nodes into the network. The DSDV and TORA routing protocols were significantly impacted by wormhole attack, leading to limited data transmission and a low packet delivery ratio. Conversely, the DSR protocol performed better, demonstrating higher average throughput, successful data transmission, and improved resistance against wormhole attack. These findings highlight the importance of selecting a robust routing protocol like DSR to mitigate the impact of attack and ensure efficient data transfer in mobile ad hoc networks.
Performance Analysis, TORA, DSDV, DSR, MANET, Protocols, Wormhole, Metrics, Attack.
Pranav Gunhal, Intelligence Coalition, Sunnyvale, California, USA
This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts, while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation.
Sentiment analysis, Twitter, Karnataka elections, Congress, BJP, transformers, Indic languages, AI, novel architectures, IndicBERT, Lok Sabha elections.
Ying Zhangchi1, Wu Xuan2, Xu Haiyang1, He Dong1, Zhou Yang3, 1Information and Communication Branch of State Grid Zhejiang Electric Power Co., LTD., Hangzhou 310000, China, 2College of Computer Science and Technology, Zhejiang University, Hangzhou 310030, China, 3State Grid Yiwu Power Supply Company, Yiwu, Zhejiang 322000, China
Accurate prediction of industrial load is a crucial step in the development of smart grids. Industrial load forecasting is fundamentally a time series forecasting problem. Traditional time series forecasting models struggle to accurately predict complex industrial load changes due to their nonlinearity, time series nature, and unstationarity. Neural network models, with their robust self-learning capabilities, can effectively process industrial load data. However, these models are prone to overfitting and uncertainty issues arising from manual parameter adjustments based on experience during training. Moreover, the inability to handle bidirectional data propagation causes conventional neural network models to lose essential load data characteristics and interrelated data information. In this paper, we propose an SSA-Dropout-Bi-LSTM prediction model based on a sliding window. First, the Bi-directional Long Short-Term Memory (Bi-LSTM) neural network theory is employed to facilitate bidirectional information transfer. Dropout technique is utilized to decrease the model's overfitting degree. The sparrow search algorithm (SSA) is further used to search for the optimal hyperparameters of the Dropout-Bi-LSTM model. The model's parameter search uncertainty is reduced by dynamically adjusting parameters through the machine learning algorithm, thereby enhancing the neural network model's generalization ability. We conducted load forecasting for six industrial users in Zhejiang province. The proposed model's Mean Absolute Percentage Error (MAPE) on the test set averages at 3.75%, an improvement compared to other combinations of LSTM models (4.17%-5.37%), and a significant enhancement compared to RNN (7.22%) and GRU (5.94%). The mean coefficient of determination (R2) of the proposed model is 94.34%, which is considerably higher than RNN (87.58%) and GRU (90.28%). In comparison, the proposed model demonstrates higher prediction accuracy and better model fitting effects.
short-term industrial load forecasting, Sparrow search algorithm, Bi-LSTM, SSA-Dropout-Bi-LSTM, Optimal hyperparameters.
Sarah Alqahtani, Haya Alubaidan, ZenabAlsadeq, Sunday Olusanya Olatunji, Department of Computer Science, College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Kingdom of Saudi Arabia
This research was motivated by the fact that around 1.5 million clients churn each year, and this number is gradually rising. Nowadays, customer churn prediction is one of the most important issues facing banking sectors related to Customer Relationship Management (CRM), where the customer is the most valuable asset for service organizations. This paper aimsto utilize machine learning algorithms Decision Tree (DT), and K-Nearest Neighbor (KNN)to predict the customers who have a high chance of unsubscribing from the bank service and shifting to another bank. These algorithms have been chosen based on related work and literature reviews that have been done in CRM to predict customer churn. As a result, this comparison has shown a slight improvement in KNN by 0.2% over DT with an accuracy of 82.5%.
Customer Relationship Management, Machine Learning, Customer Churn, Decision Tree, K-Nearest Neighbor.