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International CARDS Journal

Volume 2, Issue 4 2020 Q2



Software frameworks and software testing


A number of approaches can optimize and orchestrate software testing to identify defects early in the software development lifecycle (Li, Ren, & Wang, 2019).  Rajalingam, Vasant, Khe, Merican, and Oo (2016) describe the response surface methods as a combination of techniques for analyzing the response of several variables for optimization.  Yumoto, Matsuodani, and Tsuda (2013) avoid duplication of test cases by classifying test conditions into categories.  Li et al. (2019) compare the quantitative analysis of fault tree probabilities to a qualitative analysis of software defects.  Affonso, Carneiro, Rodrigues, and Nakagawa (2013) develop a model for automating the process of software adaptation to reduce errors and increase efficiency and speed.  Toure, Badri, and Lamontagne (2014) test and compare software build tools with k-means clustering to identify differences in attributes such as size, complexity, and coupling.


Software frameworks may assist software developers in both development and testing (Myllärniemi, Kujala, Raatikainen, & Sevońn, 2018).  Myllärniemi et al. (2018) research the adoption of software frameworks by software developers.  Baresi and Pezzè (2006) describe the quality process of reviewing the quality of the software development and software product to span the entire development life cycle.  Lawanna (2012) explain verification to ensure that the software was developed properly and validation to ensure that the software meets the requirements of the stakeholders.


 


 


Affonso, F. J., Carneiro, M. C. V. S., Rodrigues, E. L. L., & Nakagawa, E. Y. (2013). Adaptive software development supported by an automated process: A reference model. Revista de Sistemas de Informacao da FSMA, 12, 8-20.


Baresi, L., & Pezzè, M. (2006). An introduction to software testing. Electronic Notes in Theoretical Computer Science, 148(1), 89-111. doi:https://doi.org/10.1016/j.entcs.2005.12.014


Lawanna, A. (2012). The theory of software testing. AU Journal of Technology, 16(1), 35-40.


Li, H. W., Ren, Y., & Wang, L. N. (2019). Research on software testing technology based on fault tree analysis. Procedia Computer Science, 154, 754-758. doi:https://doi.org/10.1016/j.procs.2019.06.118


Myllärniemi, V., Kujala, S., Raatikainen, M., & Sevońn, P. (2018). Development as a journey: Factors supporting the adoption and use of software frameworks. Journal of Software Engineering Research and Development, 6(1), 6. doi:10.1186/s40411-018-0050-8


Rajalingam, S., Vasant, P., Khe, C. S., Merican, Z., & Oo, Z. (2016). Optimization of injection molding process parameters by response surface methods Journal of Information Technology & Software Engineering, 6(2).


Toure, F., Badri, M., & Lamontagne, L. (2014). A metrics suite for JUnit test code: A multiple case study on open source software. Journal of Software Engineering Research and Development, 2(1), 14. doi:10.1186/s40411-014-0014-6


Yumoto, T., Matsuodani, T., & Tsuda, K. (2013). A test analysis method for black box testing using AUT and fault knowledge. Procedia Computer Science, 22, 551-560. doi:https://doi.org/10.1016/j.procs.2013.09.135



Software development methodologies


Software development methodologies may measure, add, and develop value to the customer for software products (dos Santos, Beltrão, de Souza, & Travassos, 2018).  dos Santos et al. (2018) show the principles of lean methodologies as value to the customer, a value stream, a flow of continuous production, the customer’s ability to pull the product from the producer, and a cycle of the principles.  dos Santos et al. (2018) describe the principles of Kanban to include a visualized workflow, a limit on work in progress, measured and managed flow, explicit process policies, and models to identify improvement opportunities.  Wilson, Bell, Wilson, and Witteman (2018) plan a mobile health agile development and evaluation lifecycle that includes design, development, testing, clinical trial evaluations, and post-market evaluations.


Advances in software development models represent the scale and mobility of modern software development (Stoica, Ghilic-Micu, Mircea, & Uscatu, 2016).  Stoica et al. (2016) chronologize the software development models as waterfall, V, incremental, rapid application development, agile, iterative, and spiral.  Turetken, Stojanov, and Trienekens (2017) discuss scalability and integration of agile software development in large system development.  Dingsøyr, Nerur, Balijepally, and Moe (2012) suggest more theory based approaches to agile development model research.


Agile software development models may be consistent with the advance of hardware platforms and information networks (Priyan & Devi, 2019).  Priyan and Devi (2019) highlight the growth of connected devices in the Internet of Things, connected cars, wearables, smart televisions, tablets, and smartphones.  Mendivelso, Garcés, and Casallas (2018) suggest more domain specific language approaches for defining software views.  Rubinstein (2013) discusses the challenges of protecting data and creating data protection regulations with the growing landscape of big data technologies.


 


 


Dingsøyr, T., Nerur, S., Balijepally, V., & Moe, N. B. (2012). A decade of agile methodologies: Towards explaining agile software development. Journal of Systems and Software, 85(6), 1213-1221. doi:https://doi.org/10.1016/j.jss.2012.02.033


dos Santos, P. S. M., Beltrão, A. C., de Souza, B. P., & Travassos, G. H. (2018). On the benefits and challenges of using kanban in software engineering: A structured synthesis study. Journal of Software Engineering Research and Development, 6(1), 13. doi:10.1186/s40411-018-0057-1


Mendivelso, L. F., Garcés, K., & Casallas, R. (2018). Metric-centered and technology-independent architectural views for software comprehension. Journal of Software Engineering Research and Development, 6(1), 16. doi:10.1186/s40411-018-0060-6


Priyan, M. K., & Devi, G. U. (2019). A survey on internet of vehicles: Applications, technologies, challenges and opportunities. Int. J. Advanced Intelligence Paradigms, 12(1), 98-119.


Rubinstein, I. S. (2013). Big data: The end of privacy or a new beginning? International Data Privacy Law, 3(2), 74-87. doi:10.1093/idpl/ips036


Stoica, M., Ghilic-Micu, B., Mircea, M., & Uscatu, C. (2016). Analyzing agile development – from waterfall style to scrumban. Informatica Economica, 20(4).


Turetken, O., Stojanov, I., & Trienekens, J. J. M. (2017). Assessing the adoption level of scaled agile development: A maturity model for scaled agile framework. Journal of Software: Evolution and Process, 29(6), e1796. doi:10.1002/smr.1796


Wilson, K., Bell, C., Wilson, L., & Witteman, H. (2018). Agile research to complement agile development: A proposal for an mHealth research lifecycle. npj Digital Medicine, 1(1), 46. doi:10.1038/s41746-018-0053-1



Software engineering in emerging software ecosystems


Software engineering may advance with the development of emerging software ecosystems (Jansen, 2020).  Ali and Usman (2019) describe the advance of evidence based software engineering as engineering based on literature reviews a growing field in software engineering.  Jansen (2020) presents a software ecosystem governance maturity model for providing guidance on creating and advancing software ecosystem initiatives.  Yaman, Fagerholm, Munezero, Männistö, and Mikkonen (2020) identify reasons that consumers may not be involved in software development activities including complex consumer structures, budgeting constraints, privacy requirements, and lack of processes.


Machine learning and NoSQL databases are examples of emerging technologies (Abraham, Huynh, & Vu, 2019; Alotaibi & Pardede, 2019).  Abraham et al. (2019) compare the machine learning techniques of k-nearest neighbors, support vector machines, decision trees, classification bagged ensembles, and random forest.  Alotaibi and Pardede (2019) propose techniques for mapping relational databases to three types of NoSQL databases.


Blockchain technologies and Internet of Things also influence software engineering (Kim, Kim, & Huh, 2019; Uddin, Stranieri, Gondal, & Balasurbramanian, 2019).  Kim et al. (2019) describe three types of blockchains, private, public, and consortium.  Uddin et al. (2019) describe measuring sound transmission data underwater with Internet of Things sensors.  Rejeb, Keogh, and Treiblmaier (2019) discuss the application of embedded machine sensors and machine analytics in production and manufacturing operations.


Other emerging technologies include performance management systems and 3D printing (Attaran, 2017; Bheemanathini, Srinivasan, & Jayaraman, 2019).  Bheemanathini et al. (2019) compare different types of performance management systems including conventional methods and the contemporary methods of management by objectives, behavior rating, 360 degree feedback, and cost accounting.  Attaran (2017) explain two types of additive manufacturing with 3d printing, rapid prototyping and component manufacturing.


 


 


Abraham, S., Huynh, C., & Vu, H. (2019). Classification of soils into hydrologic groups using machine learning. Data, 5(1), 2.


Ali, N. b., & Usman, M. (2019). A critical appraisal tool for systematic literature reviews in software engineering. Information and Software Technology, 112, 48-50. doi:https://doi.org/10.1016/j.infsof.2019.04.006


Alotaibi, O., & Pardede, E. (2019). Transformation of schema from relational database (RDB) to NoSQL databases. Data, 4(4), 148.


Attaran, M. (2017). Additive manufacturing: The most promising technology to alter the supply chain and logistics. Journal of Service Science and Management, 189-206. doi:10.4236/jssm.2017.103017


Bheemanathini, S., Srinivasan, B., & Jayaraman, A. (2019). A review on performance management system (PMS) methods for employees appraisal in an organization. Industrial Engineering & Management, 8(2).


Jansen, S. (2020). A focus area maturity model for software ecosystem governance. Information and Software Technology, 118, 106219. doi:https://doi.org/10.1016/j.infsof.2019.106219


Kim, S.-K., Kim, U.-M., & Huh, J.-H. (2019). A study on improvement of Blockchain application to overcome vulnerability of IoT multiplatform security. Energies, 12(3). doi:10.3390/en12030402


Rejeb, A., Keogh, G. J., & Treiblmaier, H. (2019). Leveraging the Internet of Things and Blockchain technology in supply chain management. Future Internet, 11(7). doi:10.3390/fi11070161


Uddin, A. M., Stranieri, A., Gondal, I., & Balasurbramanian, V. (2019). A lightweight Blockchain based framework for underwater IoT. Electronics, 8(12). doi:10.3390/electronics8121552


Yaman, S., Fagerholm, F., Munezero, M., Männistö, T., & Mikkonen, T. (2020). Patterns of user involvement in experiment-driven software development. Information and Software Technology, 120, 106244. doi:https://doi.org/10.1016/j.infsof.2019.106244



Technology and healthcare innovations


Machine learning and mobility computing may provide for healthcare innovations that apply technology (Zheng et al., 2017).  Zheng et al. (2017) propose to detect irregularities in electronic medical records to assist in disease progression modeling.  Choi et al. (2016) present an algorithm based on neural networks for clinical prediction.  Chen, Chou, and Wu (2017) implement random forests to predict the effectiveness of treatments for chronic diseases.


Machine learning has been implemented for the purpose of clinical prediction (Pal, 2016).  Pal (2016) describes applications for data driven healthcare solutions for mobile phone detection of chronic diseases, measuring blood pressure with mobile phones and cameras, and sensor analytics based system for diagnosing stroke patients.  Martinez-Maldonado et al. (2017) introduce learning analytics innovations to healthcare by analyzing a simulated environment with multimodal analytics.  Puri et al. (2016) implement the Internet of Things for heart condition monitoring and alerts that may be able to provide early detection for heart conditions by detecting abnormal electric signals.


 Tan and Varghese (2016) suggest applying sensors, tracking devices, other Internet of Things technologies to promote healthier behavior.  Veliˇckovi´c et al. (2017) propose a method of improving privacy of patient analytics by performing computations on the end devices to prevent the need of transfer of patient data from Internet of Things technologies.  Varghese, Bukhari, Malhotra, and De (2014) explain that the availability of open source tools have made it easier to apply computer aided vision to tests of comparison of visual human interpretation in medical diagnosis.


 


 


Chen, C.-Y., Chou, C.-N., & Wu, I.-W. (2017). Toward personalized treatment of chronic diseases: The CKDCase study. Paper presented at the Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care, Mountain View, California, USA.


Choi, E., Bahadori, M. T., Searles, E., Coffey, C., Thompson, M., Bost, J., . . . Sun, J. (2016). Multi-layer representation learning for medical concepts. Paper
presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA.


Martinez-Maldonado, R., Power, T., Hayes, C., Abdiprano, A., Vo, T., Axisa, C., & Shum, S. B. (2017). Analytics meet patient manikins: Challenges in an authentic small-group healthcare simulation classroom. Paper presented at the Proceedings of the Seventh International Learning Analytics & Knowledge Conference, Vancouver, British Columbia, Canada.


Pal, A. (2016). Data-driven healthcare using affordable sensing: Screening, diagnosis and therapy. Paper presented at the Proceedings of the First Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems, Singapore, Singapore.


Puri, C., Ukil, A., Bandyopadhyay, S., Singh, R., Pal, A., & Mandana, K. (2016). iCarMa: Inexpensive cardiac arrhythmia management -- an IoT healthcare analytics solution. Paper presented at the Proceedings of the First Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems, Singapore, Singapore.


Tan, V., & Varghese, S. A. (2016). IoT-enabled health promotion. Paper presented at the Proceedings of the First Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems, Singapore, Singapore.


Varghese, F., Bukhari, A. B., Malhotra, R., & De, A. (2014). IHC profiler: An open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples. PLoS One, 9(5), e96801. doi:10.1371/journal.pone.0096801


Veliˇckovi´c, P., Lane, N. D., Bhattacharya, S., Chieh, A., Bellahsen, O., & Vegreville, M. (2017). Scaling health analytics to millions without compromising privacy using deep distributed behavior models. Paper presented at the Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, Barcelona, Spain.


Zheng, K., Wang, W., Gao, J., Ngiam, K. Y., Ooi, B. C., & Yip, W. L. J. (2017). Capturing feature-level irregularity in disease progression modeling. Paper presented at the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, Singapore.



Emerging tools in software engineering


A number of emerging tools in software engineering may facilitate software development (Kim, Seok, & Moon, 2018).  Kim et al. (2018) describe the advantage of the robot operating system and provide a qualitative analysis of the robot software platform.  Penha et al. (2018) present research on tools that provide a graphical representation of textual social relationships.  Md (2019) explores the role that quantum computing may have on natural intelligence and artificial intelligence.


Hudaib and Fakhouri (2016) present automating software fault detection and recovery by deleting defective components and modifying or replacing them.  Mindrut, Manolica, and Roman (2015) create a model for developing a brand identity and measure the impact of components of the model.  Irimia-Dieguez, Medina-Lopez, and Alfalla-Luque (2015) provide a qualitative analysis of tools for financial performance evaluations on large projects.


Internet of Things and blockchain technology may also impact software engineering (Aladem, Baek, & Rawashdeh, 2019; Casino, Dasaklis,& Patsakis, 2019).  Aladem et al. (2019) discuss implementing cameras as sensors and the advantages they have in scene segmentation and object recognition.  Casino et al. (2019) classify and compare three types of block chain networks public, private, and federated.


 


 


Aladem, M., Baek, S., & Rawashdeh, S. A. (2019). Evaluation of image enhancement techniques for vision-based navigation under low illumination. Journal of Robotics,
2019
, 15. doi:10.1155/2019/5015741


Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics
and Informatics, 36
, 55-81. doi:https://doi.org/10.1016/j.tele.2018.11.006


Hudaib, A. A., & Fakhouri, H. N. (2016). An automated approach for software fault detection and recovery. Communications and Network, 8, 158-169.


Irimia-Dieguez, A. I., Medina-Lopez, C., & Alfalla-Luque, R. (2015). Financial management of large projects: A research gap. Procedia Economics and Finance, 23,
652-657. doi:https://doi.org/10.1016/S2212-5671(15)00495-5


Kim, J.-M., Seok, H. J., & Moon, C.-b. (2018). Experimental comparison of the simultaneous localization and mapping schemes in the practical environments using the robot operating system. Journal of Automation and Control, 6(1), 15-18. doi:10.12691/automation-6-1-2


Md, S. S. (2019). Defining quantum artificial intelligence (Q.A.I). Robotics & Automation Engineering Journal (RAEJ), 5(1).


Mindrut, S., Manolica, A., & Roman, C. T. (2015). Building brands identity. Procedia Economics and Finance, 20, 393-403. doi:https://doi.org/10.1016/S2212-5671(15)00088-X


Penha,F., Miranda, E., Lucena, M., Lucena, L., Alencar, F., & Filho, C. S. (2018). Actor’s social complexity: A proposal for managing the iStar model. Journal of Software Engineering Research and Development, 6(1), 11. doi:10.1186/s40411-018-0055-3



Internet of things devices and smart cities


Internet of Things devices may be a component for the optimization and efficiency of planning and developing smart cities (Musa, 2016).  Musa (2016) explains how passive and active sensors can be controlled remotely with wired or wireless networks for city planning in smart cities.  Engin and Treleaven (2019) suggest that Internet of Things devices will improve public infrastructure with the ability to support computer aided design and secure block chain smart contracts.  Guo, Johansson, and Löndahl (2019) explore the development of cryptographic messaging codes that can be deployed on constrained hardware devices such as sensors.


The optimization of Internet of Things devices may improve analysis, efficiency, and security (Basu, Gupta, Moretti, & Gui, 2017).  Basu et al. (2017) seek to optimize energy efficiency in sensor modules by analyzing consumption in sensing, computation, and communication.  Wang et al. (2019) describe how graphical processing units have become adept at solving large scale data analysis and data mining problems.  Alohaly, Takabi, and Blanco (2019) propose how natural language processing techniques such as relation extraction can assist the implementation of access control policies.  Khraisat, Gondal, Vamplew, and Kamruzzaman (2019) survey intrusion detection systems such as signature-based, anomaly-based, and statistics-based intrusion detection for detecting and protecting from cyber-attacks.


 


 


Alohaly, M., Takabi, H., & Blanco, E. (2019). Automated extraction of attributes from natural language attribute-based access control (ABAC) Policies. Cybersecurity, 2(1), 2. doi:10.1186/s42400-018-0019-2


Basu, D., Gupta, G. S., Moretti, G., & Gui, X. (2017). Energy efficiency comparison of a state based adaptive transmission protocol with fixed power transmission for mobile wireless sensors. Journal of Telecommunications System & Management, 6(1), 1-11. doi:10.4172/2167-0919.1000149


Engin, Z., & Treleaven, P. (2019). Algorithmic government: Automating public services and supporting civil servants in using data science technologies. The Computer Journal, 62(3), 448-460. doi:10.1093/comjnl/bxy082


Guo, Q., Johansson, T., & Löndahl, C. (2019). Solving LPN using covering codes. Journal of Cryptology. doi:10.1007/s00145-019-09338-8


Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 20. doi:10.1186/s42400-019-0038-7


Musa, S. (2016). Smart cities - a roadmap for development. Journal of Telecommunications System & Management, 5(3), 1-3. doi:10.4172/2167-0919.1000144


Wang, Y., Gui, Z., Wu, H., Peng, D., Wu, J., & Cui, Z. (2019). Optimizing and accelerating space-time Ripley ’s K function based on Apache Spark for distributed spatiotemporal point pattern analysis. Future Generation Computer Systems. doi:https://doi.org/10.1016/j.future.2019.11.036