Software Changes Research Articles

Hand Gesture Based Touch-Free User Interface for Elevator/Lift

The user interfaces of control panels used for electronic automation have been limited to devices such as remote control, key pad and touch screen for a long time. These interfaces are not natural and nor flexible for users. Consumers are expecting natural efficient user interfaces. Recent improvements in sensor technology are now opening the path for developers to add gesture control to their products. This Project presents a switch control panel for Touch-free user interface using simple hand gestures technique to control operations of Elevator/Lift. This gesture-based switch control panel is not just limited to Elevator/Lift, but can also be implemented in other areas such as Vending machines, ATMs and many more with little changes in software & hardware if required. To use this interactive system based on hand gestures, one does not require any prerequisite knowledge as in the case of touch-screens, keyboard etc.

Cybersecurity Assessment Framework for Digital Interface Between Safety and Security at Nuclear Power Plants

This study aims to assess the cybersecurity aspects of nuclear power plants (NPPs) in regards to a digital safety-security interface. In traditional and advanced NPPs, safety and security functions are performed by analogue and digital systems. The risk of unauthorized software changing real-world system behaviors is a major and critical infrastructure safety and security issue. Within this context, a NPP is one of the most emblematic examples of critical infrastructure cyber targets. In this study, an evaluation of the functional impact of a cyber-attack targeting specific digital equipment in a nuclear facility is performed by means of a nuclear power plant simulator specifically developed for cybersecurity assessments, deployment of realistic cyber-attack simulation scenarios, and collection and evaluation of simulated data. The Asherah nuclear power plant simulator reproduces the dynamic behavior of a two-loop 2,772 MWt pressurized water reactor (PWR) including primary, secondary, and tertiary loops, as well as selected network infrastructure elements, communication protocols, and control systems. The system is projected to survive cyber-attacks, to allow great flexibility for digital systems and network research, and to allow the capture of data for an a posteriori analysis. The preliminary simulation results obtained by the deployment of realistic cyber-attack scenarios facilitated an understanding of the impacts of cyber-attacks, how they propagate in nuclear digital cyber-physical systems and their consequences, both in terms of plant security and safety.

Influence of control strategy on seasonal coefficient of performance for a heat pump with low-temperature heat storage in the geographical conditions of Central Europe

This paper presents a computational parametric study on increasing the Seasonal Coefficient of Performance (SCOP) for residential heat pumps. The studied system consists of a heat pump, low-temperature heat storage, and a control unit. The heat pump enables selection of a low-temperature heat source between ambient air and water in a tank. Two variants of low-temperature heat storage are tested, particularly, insulated-water heat storage and water heat storage sunken in soil. The study is further complemented with a test of selected algorithms for heat pump control: equithermal regulation, a binary algorithm for temperature source selection, a predictive algorithm for the heat storage discharging, and an algorithm for deferred heat storage discharging. A computational model of the system is made using Python. The assessment of HP operation is made based on meteorological data from the years 2008–2019 recorded in the city of Brno, Czech Republic, Central Europe. The results obtained show that using the approaches tested has the potential for increasing the SCOP. This increase reaches as much as 5.19% and it requires only a simple software change in the heat pump control algorithm and connection to meteorological data prediction.

Decentralized Remote Voting System

In Our idea we are going to build Decentralized Distributed voting platform Using Blockchain technology that could be a web application or Mobile App. Authentication: Authentication can be done by Aadhar Card using Mobile number OTP or Fingerprint on successfully authentication unique blockchain address will generate for particular voter. Data Sync: Data of portal need to be sync with voting booths. So, EVM also need to be Digitalize that connected could be connected & sync to our portal’s decentralized databases. These are Two solution to overcome vote lost still there are some percentage of vote lost because of those people who’s Aadhar Card not Updated and they not receive OTP on Mobiles. Then there is one option for Authentication that is fingerprint scan. So we want type of machine which easily available for common people and that machine have fingerprint scanner And that Machine is nothing but ATM machine, means Our third solution is some changes in ATM machine software will Convert ATM into EVM.

Digitalization in Dental Prosthesis and Comparative Analysis of IOPA and RVG techniques for Tooth Measurements

The tooth height is mostly measured during root canal procedure. It is the most common reason for taking intra oral radiography during any procedure performance. Dental radiograph is also captured to find underlying cause of dental problems like peri-apical lesions, carries, bone defects, post procedure assessment of surgery or procedure done by dentist, etc. Intra oral radiograph also helps in gathering essential information of morphological bone status that helps to determine prognosis of treatment. Dental radiographs help in selecting the treatment plan, and determining the outcomes of different therapies. The study was aimed at evaluating the parametric measurements as got through radiovisiography (RVG) and conventional (intraoral periapical [IOPA] radiographs) for the determination of height of tooth, crownlength, root length, mesiodistal diameter at crown and mesio-distal diameter at tooth root. Radiographs of12 subjects (healthy patients) were taken by conventional methods as well as digital method, 5 standard points were selected on each tooth and dimensions of 3 tooth of each subjects were recorded in mm. Total 180 sites were recorded and result was compared with previous studies done on dogs and other animals. The results didn’t show any significant statistical parametric variations in measured values between conventional and digital radiographic methods. Hence, both IOPA radiograph as well as RVG can be used, but RVG provides good images with lots of colour changes in software in lesser time with good storage facility in computers as compared to conventional methods.

Framework to Manage Software Quality on IIoT Apps

The research presents a Framework based on CMMI and ITIL methodology. It complemented with an ASQ management type, which allows incorporating metrics that directly involve both the development process of embedded systems based on IoT architectures and oriented to industry 4.0 or IIoT. The methodology is based on the development of software for Embedded Systems using SysML modeling. The scheme to allows incorporates necessary mechanisms that enable the management of the System related to service continuity and easy adaptation to changes, considering service connectivity and compatible with an industrial environment. This scheme allows quantifying through indicators each one of the quality levels, providing at the same time the practicality and the systematization of the processes, which in the end translates into a consistent mapping for the standardization of the quality related to the lines of business that imply the treatment of IoT data at industrial level. The methodology permits are simplifying the need to deal with rapid software changes, one of the essential tasks of software systems development and maintenance teams. Due to the lack of standards and regulated work schemes for the development of IoT architectures; therefore, it is intended to establish implicitly the metrics and an essential work scheme to regulate the design, progress, and development of IIoT architectures, until defining an optimal state of implementation.

Building Survivable Software Systems by Automatically Adapting to Sensor Changes

Many software systems run on long-lifespan platforms that operate in diverse and dynamic environments. If these software systems could automatically adapt to hardware changes, it would significantly reduce the maintenance cost and enable rapid upgrade. In this paper, we study the problem of how to automatically adapt to sensor changes, as an important step towards building such long-lived, survivable software systems. We address challenges in sensor adaptation when a set of sensors are replaced by new sensors. Our approach reconstructs sensor values of replaced sensors by preserving distributions of sensor values before and after the sensor change, thereby not warranting a change in higher-layer software. Compared to existing work, our approach has the following advantages: (a) ability to exploit new sensors without requiring an overlapping period of time between the new sensors and the old ones; (b) ability to provide an estimation of adaptation quality; and (c) ability to scale to a large number of sensors. Experiments on weather data and Unmanned Undersea Vehicle (UUV) data demonstrate that our approach can automatically adapt to sensor changes with 5.7% higher accuracy compared to baseline methods.

Autonomous Flying With Neuromorphic Sensing.

Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control.

Light for a Potentially Cloudy Situation: Approach to Validating Cloud Computing Tools

Light for a Potentially Cloudy Situation: Approach to Validating Cloud Computing Tools

Regulation of Cyclic Nucleotide Phosphodiesterase 1 by Endoplasmic Reticulum Stress Contributes to Homocysteine‐induced Cardiac Hypertrophy

Objectives Mechanistic understanding of the role of homocysteine in the development of cardiac hypertrophy is inadequate at present. This study aimed to elucidate the association of cyclic nucleotide phosphodiesterase 1 (PDE1) and endoplasmic reticulum (ER) stress with cardiomyocyte hypertrophy induced by homocysteine. Methods H9c2 cardiomyocytes were allocated to control group and homocysteine-treated group in the presence or absence of ER stress inhibitor tauroursodeoxycholic acid (TUDCA) or PDE1 inhibitor Lu AF58027 for 72h. In another set of experiment, H9c2 cells were transfected with specific siRNA targeting PDE1 subtypes prior to homocysteine exposure. The cell surface area was measured by phase contrast microscope and image analysis software and ultrastructural changes of the cells were observed under transmission electron microscopy. Quantitative polymerase chain reaction and western blot were employed to detect the mRNA and protein expression of the hypertrophic markers beta-smooth muscle myosin heavy chain (β-MHC) and atrial natriuretic peptide (ANP), PDE1 subtypes (PDE1A, 1B, and 1C), and ER stress molecules (GRP78, phosphor-PERK, PERK, phosphor-IRE1, IRE1, and ATF6). The intracellular levels of cyclic guanosine monophosphate (cGMP) and cyclic adenosine monophosphate (cAMP) were measured by enzyme linked immunosorbent assay. Results Homocysteine exposure resulted in an enlargement in cell size and an increase in β-MHC and ANP expression. Homocysteine induced ER stress in H9c2 cells, shown by increased expression of GRP78 and enhanced phosphorylation of PERK and IRE1, as well as dilatation and degranulation of rough ER. The expression of PDE1A and PDE1C was significantly upregulated by homocysteine. Inhibition of ER stress or PDE1 suppressed homocysteine-induced upregulation of β-MHC and ANP and alleviated cell enlargement. In addition, the protein expression of PDE1A and PDE1C in homocysteine-exposed H9c2 cells was downregulated by the ER stress inhibitor TUDCA. Silencing of PDE1C significantly inhibited homocysteine-induced cell hypertrophy whereas knockdown of PDE1A showed minor effect. Both cAMP and cGMP levels were lowered in H9c2 cells subjected to homocysteine exposure, while only cAMP content could be restored by Lu AF58027 and TUDCA. Conclusions ER stress mediates homocysteine-induced cardiomyocyte hypertrophy via upregulating PDE1 expression. Cyclic nucleotide, in particular cAMP, is the downstream mediator of the ER stress-PDE1 signaling axis in homocysteine-induced hypertrophy.

Leadership Challenges from the Deployment of Lethal Autonomous Weapon Systems

Lethal autonomous weapon systems (LAWS) – robotic weapons that have the ability to sense and act unilaterally depending on how they are programmed – will be capable of selecting targets and delivering lethality without any human interaction. This technology may still be in its infancy, but both semi-autonomous and other precursor systems are already in service. This, argues Paddy Walker, requires a material step change in both hardware and software but, once deployed, posits a significant change in how humans wage war. This article considers the behavioural and leadership challenges that arise from the deployment of such weapons and how unsupervised engagements might degrade the commander’s craft. ◼

Diversifying Focused Testing for Unit Testing

Software changes constantly, because developers add new features or modifications. This directly affects the effectiveness of the test suite associated with that software, especially when these new modifications are in a specific area that no test case covers. This article tackles the problem of generating a high-quality test suite to cover repeatedly a given point in a program, with the ultimate goal of exposing faults possibly affecting the given program point. Both search-based software testing and constraint solving offer ready, but low-quality, solutions to this: Ideally, a maximally diverse covering test set is required, whereas search and constraint solving tend to generate test sets with biased distributions. Our approach, Diversified Focused Testing (DFT), uses a search strategy inspired by GödelTest. We artificially inject parameters into the code branching conditions and use a bi-objective search algorithm to find diverse inputs by perturbing the injected parameters, while keeping the path conditions still satisfiable. Our results demonstrate that our technique, DFT, is able to cover a desired point in the code at least 90% of the time. Moreover, adding diversity improves the bug detection and the mutation killing abilities of the test suites. We show that DFT achieves better results than focused testing, symbolic execution, and random testing by achieving from 3% to 70% improvement in mutation score and up to 100% improvement in fault detection across 105 software subjects.

On the applicability of search-based algorithms for software change prediction

On the applicability of search-based algorithms for software change prediction

Code Analysis Tool to Detect Extract Class Refactoring Activity in Vb.Net Classes

Code changes due to software change requests and bug fixing are inevitable in software lifecycle. The code modification may slowly deviate the code structure from its original structure that leads to unreadable code. Even though the code structure does not affect the software behaviour, it affects the code understandability and maintainability of software. Code refactoring is typically conducted to enhance the code structure; however, this task needs a lot of developers’ effort. Thus, this paper aims at developing a tool that will help programmers identify possible code refactoring.Weconsider two aspects of refactoring:(i) refactoring activities, and (ii) refactoring prediction model. In terms of refactoring activity, we focus on Extract Class. The object-oriented metrics are used to predict the possibility of code refactoring. The combination of two refactoring aspects recommends the possible refactoring effort and identify classes that are involved. As a result, we managed to get 79% percent of accuracy based on the 11 correct results out of 14 that the tool correctly detected. On top of supporting programmers in improving codes, this work also may give more insight into how refactoring improvessystems.

Quantitative Software Change Prediction in Open Source Web Projects Using Time Series Forecasting

Software change prediction (SCP) is used for the prediction of changes earlier in the software development life cycle. It identifies the files that are change prone. Software maintenance costs can be reduced with the help of accurate prediction of change-prone files. Most of the literature of SCP deals with the identification of a class as change prone or not change prone. In the present work, the amount of change in a web project in terms of line of code added (loc_added), line of code deleted (loc_deleted), and lines of code (LOC) are predicted using time series forecasting method of machine learning. Data of web projects is obtained from GIT repository using Pydriller Python package extractor. The obtained result showed that support vector machine (SVM) is good for prediction of loc_added and loc_removed while the random forest is good for the prediction of LOC. Results advocate the use machine learning techniques for forecasting changes amount in web projects.

A software availability model based on multilevel software rejuvenation and markov chain

Increasing use of software, rapid and unavoidable changes in the operational environment bring many problemsfor software engineers. One of these problems is the aging and degradation of software performance. Software rejuvenationis a proactive and preventive approach to counteract software aging. Generally, when software is initiated, amounts ofmemory are allocated. Then, the body of software is executed for providing a service and when the software is terminated,the allocated memory is released. In this paper, a rejuvenation model based on multilevel software rejuvenation andMarkov chain presented. In this model, the system performance as a result of degraded physical memory and memoryusage is divided into four equal levels by services. Hence, we offer four types of policies for software rejuvenation. Inaddition, the system availability is determined, and a cost function for the model is introduced. The cost functionincludes the time of performing rejuvenation, the number of system services at any time, and the number of rejuvenationactions. To validate the proposed model, a case study in the banking system in Iran has been studied. Due to thedifferences in the use of the system over time, it is better to perform the four different policies with regard to the useof the system. The numerical results show that the proposed model is convenient for the system so that the costs arereduced per day.

Human Competency Assessment for Software Configuration Management

Late product delivery is a common problem in software projects. Late delivery can be attributed to software change especially in the development and maintenance phases, leading to more work than originally planned, thus increasing project completion time. One approach for managing software change is Software Configuration Management (SCM). SCM was first introduced in the 1970s to ensure the timely delivery of software products. To date, SCM implementation is supported by recognized standards, best practice, and countless commercial and proprietary tools. However, after more than 50 years, the issue of late software product delivery still prevails, questioning the practice of SCM in software organizations. One aspect of SCM that has received little focus in mainstream research is the human aspects. This study aims to identify how the competency of SCM practitioners can be assessed through the identification of SCM competency criteria and the development of a competency assessment framework. The framework was validated through expert reviews and case studies involving practitioners from the public sector, industry, institutes of higher learning and international organizations. Results confirmed the reliability of the SCM competency criteria and the plausibility of the competency assessment framework.

DEJIT: A Differential Evolution Algorithm for Effort-Aware Just-in-Time Software Defect Prediction

Software defect prediction is an effective approach to save testing resources and improve software quality, which is widely studied in the field of software engineering. The effort-aware just-in-time software defect prediction (JIT-SDP) aims to identify defective software changes in limited software testing resources. Although many methods have been proposed to solve the JIT-SDP, the effort-aware prediction performance of the existing models still needs to be further improved. To this end, we propose a differential evolution (DE) based supervised method DEJIT to build JIT-SDP models. Specifically, first we propose a metric called density-percentile-average (DPA), which is used as optimization objective on the training set. Then, we use logistic regression (LR) to build a prediction model. To make the LR obtain the maximum DPA on the training set, we use the DE algorithm to determine the coefficients of the LR. The experiment uses defect data sets from six open source projects. We compare the proposed method with state-of-the-art four supervised models and four unsupervised models in cross-validation, cross-project-validation and timewise-cross-validation scenarios. The empirical results demonstrate that the DEJIT method can significantly improve the effort-aware prediction performance in the three evaluation scenarios. Therefore, the DEJIT method is promising for the effort-aware JIT-SDP.

Evolution of existing software to mobile computing platforms: Framework support and case study

Mobile computing as ubiquitous and pervasive technology supports portable and context-aware computation. To date, there exist a significant number of traditional computing systems–running on the web and/or workstation-based platforms–that lack features of mobile computing, including but not limited to ubiquity, context-sensing, and high interactivity. Software that executes on these traditional computing systems is referred to as legacy software that can be upgraded to exploit the features of mobile technologies. However, legacy software may contain critical data, logic, and processes that cannot be easily replaced. One of the solutions is to evolve legacy software systems by (a) upgrading their functionality while (b) preserving their data and logic. Recently research and development efforts are focused on modernizing the legacy systems as per the needs of service and cloud-based platforms. However, there does not exist any research that supports a systematic modernization of legacy software as per the requirements of the mobile platforms. We propose a framework named Legacy-to-Mobile as a solution that supports an incremental and process-driven evolution of the legacy software to mobile computing software. The proposed Legacy-to-Mobile framework unifies the concepts of software reverse engineering (recovering software artifacts) and software change (upgrading software artifacts) to support the legacy evolution. The framework follows an incremental approach with four processes that include (i) evolution planning, (ii) architecture modeling, (iii) architecture change, and (iv) software validation of mobile computing software. The framework provides the foundation (as part of futuristic research) to develop a tool prototype that supports automation and user decision support for incremental and process-driven evolution of legacy software to mobile computing platforms.

Airborne Software Change Control Process and Strategies of Civil Aircraft

In the modern civil aircraft, the number and complexity of airborne software are increasing rapidly. The software may change at any stage of the development process and the change process should be under control. This article analyzes the basic unit of software change control and the configuration identification used for airborne software, then introduces a process and strategies of software change control for civil aircraft.