Traditional quantitative research methods of data collection in programming, such as questionnaires and interviews, are the most common approaches for researchers in this field. However, in recent years, eye-tracking has been on the rise as a new method of collecting evidence of visual attention and the cognitive process of programmers. Eyetracking has been used by researchers in the field of programming to analyze and understand a variety of tasks such as comprehension and debugging. In this paper, we will focus on reporting how experiments that used eye-trackers in programming research are conducted, and the information that can be collected from these experiments. In this mapping study, we identify and report on 63 studies, published between 1990 and June 2017, collected and gathered via manual search on digital libraries and databases related to computer science and computer engineering. Among the five main areas of research interest are program comprehension and debugging, which received an increased interest in recent years, non-code comprehension, collaborative programming and requirements traceability research, which had the fewest number of publications due to possible limitations of the eye-tracking technology in this type of experiments. We find that most of the participants in these studies were students and faculty members from institutions of higher learning, and while they performed programming tasks on a range of programming languages and programming representations, we find Java language and UML representation to be the most used materials. We also report on a range of eye-trackers and attention tracking tools that have been utilized, and find Tobii eye-trackers to be the most used devices by researchers.

Swarm robotic systems is still a new field of study, and exploration of its applications and making use of its advantages can open the door for more research on this field in the near future. In swarm robotic systems, a number of simple robots can perform complex tasks efficiently than a single robot, giving robustness and flexibility to the group. However, robustness is one of the issues that need to be resolved as most of time the robots are suffering from low energy while performing the task. The main objectives of this paper are to highlight the robustness issue in swarm robotic systems and propose a solution to allow swarm robots to remain robust on achieving its task. To demonstrate the problem, foraging algorithm, which is inspired by ant’s behaviour, is simulated to highlight the problem of low energy in swarm robotic system and its effect on its robustness. One of the solutions is mainly by using power stations or banks, but both have its own limitation which are highlighted and discussed in this paper. Finally, the paper also explains on a potential mechanism, inspired by an immune system response, that will help swarm robots overcome the problem of low energy.

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Applying the fault diagnosis techniques to twisted pair copper cable is beneficial to improve the stability and reliability of internet access in Digital Subscriber Line (DSL) Access Network System. The network performance depends on the occurrence of cable fault along the copper cable. Currently, most of the telecommunication providers monitor the network performance degradation hence troubleshoot the present of the fault by using commercial test gear on-site, which may be resolved using data analytics and machine learning algorithm. This paper presents a fault diagnosis method for twisted pair cable fault detection based on knowledge-based and data-driven machine learning methods. The DSL Access Network is emulated in the laboratory to accommodate VDSL2 Technology with various types of cable fault along the cable distance between 100 m to 1200 m. Firstly, the line operation parameters and loop line testing parameters are collected and used to analyze. Secondly, the feature transformation, a knowledge-based method, is utilized to pre-process the fault data. Then, the random forests algorithms (RFs), a data-driven method, are adopted to train the fault diagnosis classifier and regression algorithm with the processed fault data. Finally, the proposed fault diagnosis method is used to detect and locate the cable fault in the DSL Access Network System. The results show that the cable fault detection has an accuracy of more than 97%, with less minimum absolute error in cable fault localization of less than 11%. The proposed algorithm may assist the telecommunication service provider to initiate automated cable faults identification and troubleshooting in the DSL Access Network System.

Asymmetrical Digital Subscriber Line (ADSL) is the technology widely deployed worldwide, but its performance may be limited with respect to its intrinsic. The nature of the copper cable causes it to be more susceptible to signal degradation and faulty line. Common ADSL line faults are short-wired fault, open-wired fault, bridge taps, and uneven pair. However, ADSL technology is still one of the most established networks, and users in the suburban area still depend on the technology to access the internet service. This paper discussed and compared a machine learning algorithm based on Decision Trees (J48), K-Nearest Neighbor, Multi-level Perceptron, Naïve Bayes, Random Forest, and Sequential Minimal Optimization (SMO) for ADSL line impairment that affects the line operation performance concerning their percentage of accuracy. Resulting from classifications done using algorithms as mentioned above, the random forest algorithm gives the highest overall accuracy for the ADSL line impairment dataset. The best algorithm for classifying DSL line impairment is chosen based on the highest accuracy percentage. The accomplishment classification of fault type in the ADSL copper access network project may benefit the telecommunication network provider by remotely assessing the network condition rather than on-site.