Software Issues Research Articles

Improving the quality of software issue report descriptions in Turkish: An industrial case study at Softtech

Improving the quality of software issue report descriptions in Turkish: An industrial case study at Softtech

TECHNOLOGY OF ADDITIVE MANUFACTURING: A COMPREHENSIVE REVIEW

The process of additive manufacturing (AM), commonly known as 3D printing, is a method of constructing a component by progressively adding material in layers using digital 3D design information. As part of 'Industry 4.0,' many industrial technologies are rapidly increasing to thrive in the twenty-first century. This study goes over seven different types of additive manufacturing in great detail. These technologies make it possible to make complex, high-value parts quickly and in small quantities without using as much energy or material or making as many tools as subtractive manufacturing does. Besides, AM also possesses some particular challenges, like post-processing, material unavailability, software issues, etc. The application of AM is expanding rapidly from micro to macro-scale sectors. 3D printing technology will change industrial operations in the following years. Eventually, the elected technology will be closely related to the proposed function.

Computer detection intelligent mining algorithm for software defined networks

The network security (NS) issues of software defined network (SDN) are becoming increasingly prominent. Traditional computer detection (for convenience, computer detection is abbreviated to CD) methods are usually based on rules and features, and cannot cope with new threats and unknown attacks. The purpose of this article is to analyze a CD intelligent mining algorithm (for convenience, intelligent mining algorithm is abbreviated to IMA) suitable for SDNs, and to optimize and improve the data acquisition difficulty, large data volume, dynamism, timeliness, and security issues of SDNs. On the basis of data preprocessing on the KDD (Data Mining and Knowledge Discovery) Cup 1999 dataset, this article constructed an architecture for CD IMAs. In the data intelligence layer, a combination of k-means algorithm and XGBoost (eXtreme Gradient Boosting) algorithm was used for computer network attack detection based on stream classification. Based on the preprocessed dataset, the k-means algorithm and its combination were trained and simulated. The effectiveness and performance optimization of the CD IMA analyzed in this article were verified by comparing it with traditional computer attack detection algorithms in terms of detection accuracy, CPU (Central Processing Unit) occupancy, detection time, and false detection rate. The experimental results showed that the accuracy of the CD IMAs analyzed in this article was over 80 %, and some even reached over 90 %. However, the accuracy of traditional computer attack detection algorithms has not reached 90 %. The detection time of traditional computer attack detection algorithms was all above 2 s, but some of the detection time of the algorithm in this article was within 2 s. The detection rate of the algorithms analyzed in this article was above 90 %, and the detection results were more accurate than traditional algorithms. This proved that the algorithm analyzed in this article can more effectively select optimized feature subsets, has good flow classification ability, and has high detection accuracy for different types of attacks. The CD IMA for SDNs has great potential and application prospects.

Challenges and Barriers of Online Teaching: Perspective of Faculty in a Private Medical College of Lahore

Objective: To identify the challenges and barriers faced during initiation of online teaching by the faculty of a private medical college of Lahore. Methodology: This cross-sectional study was conducted from Sep-Dec, 2021 in FMH College of Medicine and Dentistry, Lahore. List of faculty members was obtained from the Human Resource Department amounting to 140, a sample size of 70 was selected which was the half of total sample. A predesigned, structured questionnaire was used. Approval from IRB committee and consent was obtained. Data was collected online using Google Performa via email, analyzed using SPSS version 20 and Microsoft Excel. Percentages, proportions and means were calculated.Results: The respondents included 23 (32%) Demonstrators, 11 (15.7%) Senior Demonstrators, 22 (31.4%) Assistant Professors, 7 (10%) Associate Professors and 7 (10%) Professors. Year wise distribution of faculty was as follows; 13 (18.5%) were teaching 1st year, 9 (12.8%) 2nd year, 19 (27%) 3rd year, 25 (35 .7%) 4th year, and 4 (5.7%) final year MBBS respectively. Mean age in years was 39±9.8 of the enrolled faculty. Predominance of females 47 (67.1%) to males 23 (32.9%) was observed. Maintaining class decorum and controlling students' plagiarism was a major challenge for 65 participant s(92.9%). Inadequate online teaching resources were recognized by 61 (87.1%) to be a challenge making it difficult to inculcate clinical skills while, 58 (82.9%) accepted effectiveness of lecture was interfered by lack of face to face interaction. Most common barrier 60 (85.7%) was connectivity issues, software failure, power issues and 57(81.40) complained of lack of finances.Conclusion: Lack of face to face interaction, connectivity, software and power failure issues, in addition to finances and time management were major challenges and barriers faced during initiation of online teaching by the faculty.

Machine learning models for condition-based maintenance with regular truncated signals

Condition-based maintenance (CBM) of industrial machines depends on the continuous, real-time monitoring of the machine’s operational condition via smart sensors attached to different components on the machine. The problem of regularly spaced missing data, which can occur due to a variety of hardware or software issues, is one that is often overlooked in the literature surrounding CBM in industrial machines. Such missing data can cause issues in interpreting the true operational state of the machine, which can reduce the effectiveness of CBM processes. In this paper, we examine the capabilities of five data imputation techniques for handling this regular missing data and examine the impact these techniques have on machine learning (ML) classification algorithms for machine fault diagnosis. We examine the following techniques: simple mean imputation, mean imputation with outliers removed, best and worst-case imputation, and previous day imputation. Each of these methods is configured with the specific parameters that they will only consider data from the previous 24 hours, to ensure that the data is recent, and adequately represents the current status of the machine. The efficacy of each method at accurately reconstructing the missing data and the impact they have on ML classification is recorded in the results. The models are evaluated on a real-world dataset and are evaluated on a variety of common performance metrics.

Performance Evaluation and Real-world Challenges of IoT-Based Smart Fuel Filling Systems with Embedded Intelligence

Integrating the Internet of Things (IoT) with smart fueling systems has the potential to revolutionize the fuel industry, leading to better resource management and increased operational efficiency. With the increasing integration of machine learning techniques, these systems are capable of self-learning, adaptation, and predictive decision making. However, the effectiveness of these advanced systems in real-life situations remains an area of intense interest and research. in operational efficiency and reduces resource waste by 10% compared to conventional systems. Additionally, system bottlenecks were identified mainly in data trans- mission (delayed by up to 20% in high traffic cases) and hardware malfunctions due to environmental factors. End user feedback indicates a satisfaction level of 85%, with an emphasis on system responsiveness and fuel prediction recommendations. Challenges mainly come from software issues, unwanted environmental interference and ’some initial resistance from users accustomed to conventional systems. However, with data in hand, the benefits of integrating intelligence into IoT-based fueling systems offer a sustainable and efficient future for the fuel industry. Recommendations are made to improve data transmission channels, develop robust hardware for extreme conditions, and conduct targeted user education campaigns.

On the issue of software for environmental information systems

The increase in the number of sources of pollutants into the atmospheric air is one of the largest environmental problems in the modern world. In order to control emissions of harmful substances, calculations of maximum permissible concentrations of pollutants are used. Calculations of emission sources and maximum permissible concentrations are quite complex, and it is impractical to carry them out manually, since the calculation process takes a lot of time, while there is a high probability of errors. To simplify such a task, special software tools are used – unified programs for calculating atmospheric pollution (UPRZA). The article is devoted to the issues of increasing the level of automation of calculations of emission sources and maximum permissible concentrations using UPRZA. The issues of the standard structure of the UPRSA are considered, input parameters, algorithms for calculating the parameters of the emission source and maximum permissible concentrations are determined in accordance with the methods used. Methods of increasing the level of automation are proposed. The analysis of possible ways of development of these software complexes is carried out. The article describes the process of developing a unified program for calculating atmospheric pollution, the main advantages of its application.

Defect Prediction via Tree-Based Encoding with Hybrid Granularity for Software Sustainability

Defects in software may result in system crashes, sluggish performance, or even deadlock, leading to the depletion of valuable resources. Implementing defect prediction can assist quality assurance teams in identifying potential software issues and rationalizing the allocation of testing resources, thereby decreasing the elimination of resources and enhancing software sustainability. Researchers have recently incorporated deep learning into defect prediction, extracting structural-semantic features from codes' abstract syntax trees (ASTs). However, inappropriate node granularity in ASTs may adversely impact the effectiveness of the extracted features. In addition, converting AST nodes into integer vectors may lead to the loss of structure information, resulting in poor model predictive capability. This paper proposes a tree-based encoding method with hybrid granularity for defect prediction to address these challenges. Specifically, five granularity selection schemes are extended to generate various ASTs from codes. Subsequently, a tree-based continuous bag-of-words model is utilized to map nodes of ASTs into numeric vector representations that conform to the tree-like structure of codes. The matrices converted from ASTs are then fed into a convolutional neural network to extract program features automatically. Experiments involving 24 versions of open-source projects demonstrate that our method can improve the effectiveness of extracted features in defect prediction tasks.

A PT-based approach to construct efficient provenance graph for threat alert investigation

The Provenance Graph, constructed based on audit logs, encapsulates a wealth of causal relationships. When existing threat detection software issues an Event of Alert (EOA), security analysts conduct threat alert investigations based on the provenance graph. However, a provenance graph comprising tens of thousands of nodes and edges can lead to substantial time overhead. Moreover, coarse-grained audit logs can cause a dependency explosion in the provenance graph, thereby generating false causal relationships and interfering with the accuracy of the results. Hardware-assisted Processor Tracing (PT) can address the problem of false causal relationships caused by dependency explosion, by extracting the execution trace of a process and restoring instruction-level data flow. Leveraging PT features, we propose an efficient scheme, termed as GETSUS, which can reduce the size of the provenance graph while fully preserving the event sequences leading to the EOA. Empirical evidence shows that GETSUS can shrink a large provenance graph (approximately 200,000 edges) to a small one (around 50 edges). Compared to other schemes, the partial provenance graph output by GETSUS can fully retain the semantics related to the EOA, while improving the reduction efficiency by about tenfold.

Help Desk Support Ticket and Issue Management

IT companies require methodical approaches to handle the growing volume of customer-reported software issues and service requests. An enormous backlog of unresolved issues has the potential to drastically raise software development and maintenance expenses. An         IT company therefore need a clearly defined customer support model. It acts as a link between the client and the business from beginning to end. The support industry also used this to track and record any procedures and solutions used to close tickets. With the Helpdesk Ticketing System, you can use an online platform to solve questions in a web-based environment. To ensure seamless and efficient operation, the user can submit tickets for even the smallest questions. Both within the company and remotely, the ticketing tool is usable. which can be accessed by anyone in organization or end user.

Development and quality appraisal of a new English breast screening linked data set as part of the age, test threshold, and frequency of mammography screening (ATHENA-M) study.

To build a data set capturing the whole breast cancer screening journey from individual breast cancer screening records to outcomes and assess data quality. Routine screening records (invitation, attendance, test results) from all 79 English NHS breast screening centres between January 1, 1988 and March 31, 2018 were linked to cancer registry (cancer characteristics and treatment) and national mortality data. Data quality was assessed using comparability, validity, timeliness, and completeness. Screening records were extracted from 76/79 English breast screening centres, 3/79 were not possible due to software issues. Data linkage was successful from 1997 after introduction of a universal identifier for women (NHS number). Prior to 1997 outcome data are incomplete due to linkage issues, reducing validity. Between January 1, 1997 and March 31, 2018, a total of 11 262 730 women were offered screening of whom 9 371 973 attended at least one appointment, with 139 million person-years of follow-up (a median of 12.4 person years for each woman included) with 73 810 breast cancer deaths and 1 111 139 any-cause deaths. Comparability to reference data sets and internal validity were demonstrated. Data completeness was high for core screening variables (>99%) and main cancer outcomes (>95%). The ATHENA-M project has created a large high-quality and representative data set of individual women's screening trajectories and outcomes in England from 1997 to 2018, data before 1997 are lower quality. This is the most complete data set of English breast screening records and outcomes constructed to date, which can be used to evaluate and optimize screening.

SynDroid: An adaptive enhanced Android malware classification method based on CTGAN-SVM

Android mobile phones have the highest market share nowadays, bringing a boom of Android application programming as well as malicious software (malware) issues. Traditional machine learning and deep learning methods are widely used in Android malware detection and classification, both require plenty of application samples to train classifiers. However, the collected samples are always imbalanced, because the distribution of malware categories differs hugely in the real environment. For Android malware samples with high dimension features, and Class Imbalance Ratio coming to more than 100:1, traditional methods become weak. To fill the gap, we propose an Android malware classification model named SynDroid. The core step of SynDroid uses CTGAN-SVM to generate qualified high-dimension samples, and adaptively discards bad results. Besides, we propose KS-CIR test to help the model to determine which classes of data needed to be enhanced most. This new proposed test can measure the data in respect of both samples' quality and quantity. Lastly, Random Forest is taken as a classifier to finish the classification task. The performance of SynDroid is evaluated on CCCS-CIC-AndMal2020 on the accuracy, precision, recall and F1-score. Both longitudinal and horizontal comparison experiments have been done with traditional oversampling methods, cost-sensitive learning, and other complicated methods. The result shows that the proposed method gets 12% more accuracy than the method on the same dataset and alleviates imbalanced data problems.

Comparison of patient setup accuracy for optical surface-guided andX-ray-guided imaging with respect to the impact on intracranial stereotactic radiotherapy.

The objective of this work is to estimate the patient positioning accuracy of asurface-guided radiation therapy (SGRT) system using an optical surface scanner compared to an X‑ray-based imaging system (IGRT) with respect to their impact on intracranial stereotactic radiotherapy (SRT) and intracranial stereotactic radiosurgery (SRS). Patient positioning data, both acquired with SGRT and IGRT systems at the same linacs, serve as abasis for determination of positioning accuracy. Atotal of 35patients with two different open face masks (578 datasets) were positioned using X‑ray stereoscopic imaging and the patient position inside the open face mask was recorded using SGRT. The measurement accuracy of the SGRT system (in a"standard" and an SRS mode with higher resolution) was evaluated using both IGRT and SGRT patient positioning datasets taking into account the measurement errors of the X‑ray system. Based on these clinically measured datasets, the positioning accuracy was estimated using Monte Carlo (MC) simulations. The relevant evaluation criterion, as standard of practice in cranial SRT, was the 95th percentile. The interfractional measurement displacement vector of the SGRT system, σSGRT, in high resolution mode was estimated at 2.5 mm (68th percentile) and 5 mm (95th percentile). If the standard resolution was used, σSGRT increased by about 20%. The standard deviation of the axis-related σSGRT of the SGRT system ranged between 1.5 and 1.8 mm interfractionally and 0.5 and 1.0 mm intrafractionally. The magnitude of σSGRT is mainly due to the principle of patient surface scanning and not due to technical limitations or vendor-specific issues in software or hardware. Based on the resulting σSGRT, MC simulations served as ameasure for the positioning accuracy for non-coplanar couch rotations. If an SGRT system is used as the only patient positioning device in non-coplanar fields, interfractional positioning errors of up to 6 mm and intrafractional errors of up to 5mm cannot be ruled out. In contrast, MC simulations resulted in apositioning error of 1.6 mm (95th percentile) using the IGRT system. The cause of positioning errors in the SGRT system is mainly achange in the facial surface relative to adefined point in the brain. In order to achieve the necessary geometric accuracy in cranial stereotactic radiotherapy, use of an X‑ray-based IGRT system, especially when treating with non-coplanar couch angles, is highly recommended.

Evaluating a cutting-edge augmented reality-supported navigation system for spinal instrumentation

ObjectiveDorsal instrumentation using pedicle screws is a standard treatment for multiple spinal pathologies, such as trauma, infection, or degenerative indications. Intraoperative three-dimensional (3D) imaging and navigated pedicle screw placement are used at multiple centers. For the present study, we evaluated a new navigation system enabling augmented reality (AR)-supported pedicle screw placement while integrating navigation cameras into the reference array and drill guide. The present study aimed to evaluate its clinical application regarding safety, efficacy, and accuracy.MethodsA total of 20 patients were operated on between 06/2021 and 01/2022 using the new technique for intraoperative navigation. Intraoperative data with a focus on accuracy and patient safety, including patient outcome, were analyzed. The accuracy of pedicle screw placement was evaluated by intraoperative CT imaging.ResultsA median of 8 (4–18) pedicle screws were placed in each case. Percutaneous instrumentation was performed in 14 patients (70%). The duration of pedicle screw placement (duration scan–scan) was 56 ± 26 (30–107) min. Intraoperative screw revision was necessary for 3 of 180 pedicle screws (1.7%). Intraoperatively, no major complications occurred—one case of delay due to software issues and one case of difficult screw placement were reported.ConclusionThe current study's results could confirm the use of the present AR-supported system for navigated pedicle screw placement for dorsal instrumentation in clinical routine. It provides a reliable and safe tool for 3D imaging-based pedicle screw placement, only requires a minimal intraoperative setup, and provides new opportunities by integrating AR.

Medical cyber crises and biotechnological syndromes: a multisite clinical simulation study focused on digital health complaints

BackgroundBiotechnological syndromes refer to the illnesses that arise at the intersection of human physiology and digital technology. Implanted technologies can malfunction (eg, runaway pacemakers, hacked insulin pumps), and consumer technologies can be exploited to impose adverse health effects (eg, technology-facilitated abuse, hacks on epilepsy websites inducing seizures). Through a series of clinical simulation events, our study aimed to (1) evaluate the ability of physicians to respond to biotechnological syndromes, (2) explore gaps in training impeding effective patient care in digital cases, and (3) identify clinical cases due to digital technology arising in the population. MethodsThis was a multisite clinical simulation study. Between Jan 1 and July 1, 2023, four half-day clinical simulation events focused on digital pathologies were delivered across three NHS sites in London and the East Midlands. Participants (n=14) ranged in seniority from clinical medical students through to hospital consultants. Ethics approval was attained from University College London. Participant performance was scored by one researcher, using mark schemes built from the Objective Structured Clinical Examinations (OSCEs) format of UK Medical Schools. Qualitative and quantitative feedback was collected from participants following each of the four scenarios. Participants were asked to identify clinical challenges present in each simulation, discuss cases within their own practice, and evaluate the usefulness of the educational material. FindingsParticipants reported a wide range of examples within their own practice (eg, insulin pumps malfunctioning due to Apple watches, cardiac arrests due to faults in ventilators). Participants described barriers to treatment in simulations, including low diagnostic suspicion of technological failures, little education on biotechnological mechanisms, a lack of available expertise, and uncertainty regarding effective therapeutics. In the subjective feedback, participants reported the lowest levels of confidence when managing cases relating to software issues in medical devices, both in terms of confidence in their own ability to deliver care (mean scores: 3·6/10 junior staff, 5·8/10 senior staff) and in their teams (3·8/10 juniors, 6·8/10 seniors). InterpretationIn our digital society, clinical cases related to technology are likely to increase in the population. At present, a lack of clinical awareness, education, training material, and appropriate guidelines are some of the barriers that health-care professionals face when treating these patients. FundingNone.

Integrated dual axis solar tracker using a single tracking motor

Solar energy is generally available in copious amounts globally and as a renewable energy resource, its importance now and in the future cannot be gainsaid. However, optimizing the rays of the sun by utilizing photovoltaic (PV) systems and aligning solar panels in a particular direction with regards to the sun’s rays always present their own challenges. Solar heating, molten salt power plants, and artificial photosynthesis are all examples of ever-evolving technologies that harness the sun's radiant light and heat for electrical energy. Solar trackers aim solar panels or modules directly at the sun. In this paper, a solar tracking system using Arduino is built. This system captures free solar energy, stores it in a battery, and then transforms it to the appropriate alternating current. It allows the energy to be used as a standalone power source in ordinary households. This system is built to adapt to its surroundings as quickly as possible. The system ensures that any software and hardware issues are minimized or wholly removed. Our system is put through its paces in terms of real-time responsiveness, reliability, stability, and security. Weather, temperature, and mild mechanical stresses are all factors that our system is built to withstand. The solar tracking system is the most effective technology to improve the efficiency of solar panels by tracking and following the sun's movement. With the help of this system, solar panels can improve the way of sunlight detection so that more electricity can be collected as solar panels can maintain a sunny position. Thus, this paper discusses the development of two-axis solar-tracking development using Arduino Uno as the principal controller of the system.

Beyond mapping: extend the role of cartographers to user interface designers in the Metaverse using virtual reality, augmented reality, and mixed reality

ABSTRACT Dr. Buttenfield led the user evaluation and user interface design/improvement tasks in the Alexandra Digital Library (ADL) Project from 1994 to 1998 funded by the National Science Foundation in the U.S. Her research efforts in the ADL extended the role of cartographers from traditional map designers to web-based user interface and user experiences (UI/UX) designers. This paper will highlight key concepts and design principles (including user center design (UCD), usability testing, and user interface evaluation) in the ADL project and re-investigate these pioneering research topics in cartography with recent development of the Metaverse and human-computer interaction (HCI) using Extended Reality (XR), including Virtual Reality, (VR), Augmented Reality (AR), and Mixed Reality (MR). The new role of cartographers today includes user interface designers and usability evaluators using XR-enabled mapping tools and technologies. This paper will discuss cartographic research challenges and opportunities in the era of the Metaverse and highlight some software issues for cartographers to develop 3D or 4D web GIS applications.

SUMMIT: Scaffolding Open Source Software Issue Discussion Through Summarization

Issue Tracking Systems (ITS) often support commenting on software issues, which creates a space for discussions centered around bug fixes and improvements to the software. For Open Source Software (OSS) projects, issue discussions serve as a crucial collaboration mechanism for diverse stakeholders. However, these discussions can become lengthy and entangled, making it hard to find relevant information and make further contributions. In this work, we study the use of summarization to aid users in collaboratively making sense of OSS issue discussion threads. Through an empirical investigation, we reveal a complex picture of how summarization is used by issue users in practice as a strategy to help develop and manage their discussions. Grounded on the different objectives served by the summaries and the outcome of our formative study with OSS stakeholders, we identified a set of guidelines to inform the design of collaborative summarization tools for OSS issue discussions. We then developed SUMMIT, a tool that allows issue users to collectively construct summaries of different types of information discussed, as well as a set of comments representing continuous conversations within the thread. To alleviate the manual effort involved, SUMMIT uses techniques that automatically detect information types and summarize texts to facilitate the generation of these summaries. A lab user study indicates that, as the users of SUMMIT, OSS stakeholders adopted different strategies to acquire information on issue threads. Furthermore, different features of SUMMIT effectively lowered the perceived difficulty of locating information from issue threads and enabled the users to prioritize their effort. Overall, our findings demonstrated the potential of SUMMIT, and the corresponding design guidelines, in supporting users to acquire information from lengthy discussions in ITSs. Our work sheds light on key design considerations and features when exploring crowd-based and machine-learning-enabled instruments for asynchronous collaboration on complex tasks such as OSS development.

Redesigning Graphical User Interface of Open-Source Geospatial Software in a Community-Driven Way: A Case Study of GRASS GIS

Learning to use geographic information system (GIS) software effectively may be intimidating due to the extensive range of features it offers. The GRASS GIS software, in particular, presents additional challenges for first-time users in terms of its complex startup procedure and unique terminology associated with its data structure. On the other hand, a substantial part of the GRASS user community including us as developers recognized and embraced the advantages of the current approach. Given the controversial nature of the whole issue, we decided to actively involve regular users by conducting several formal surveys and by performing usability testing. Throughout this process, we discovered that resolving specific software issues through pure user-centered design is not always feasible, particularly in the context of open-source scientific software where the boundary between users and developers is very fuzzy. To address this challenge, we adopted the user-centered methodology tailored to the requirements of open-source scientific software development, which we refer to as community-driven design. This paper describes the community-driven redesigning process on the GRASS GIS case study and sets a foundation for applying community-driven design in other open-source scientific projects by providing insights into effective software development practices driven by the needs and input of the project’s community.

An Innovative Approach for Predicting Software Defects by Handling Class Imbalance Problem

From last decade unbalanced data has gained attention as a major challenge for enhancing software quality and reliability. Due to evolution in advanced software development tools and processes, today’s developed software product is much larger and complicated in nature. The software business faces a major issue in maintaining software performance and efficiency as well as cost of handling software issues after deployment of software product. The effectiveness of defect prediction model has been hampered by unbalanced data in terms of data analysis, biased result, model accuracy and decision making. Predicting defects before they affect your software product is one way to cut costs required to maintain software quality. In this study we are proposing model using two level approach for class imbalance problem which will enhance accuracy of prediction model. In the first level, model will balance predictive class at data level by applying sampling method. Second level we will use Random Forest machine learning approach which will create strong classifier for software defect. Hence, we can enhance software defect prediction model accuracy by handling class imbalance issue at data and algorithm level.