Software Release Research Articles

An optimal scheduling policy for upgraded software with updates

PurposeIn the current market scenario, software upgrades and updates have proved to be very handy in improving the reliability of the software in its operational phase. Software upgrades help in reinventing working software through major changes, like functionality addition, feature enhancement, structural changes, etc. In software updates, minor changes are undertaken which help in improving software performance by fixing bugs and security issues in the current version of the software. Through the current proposal, the authors wish to highlight the economic benefits of the combined use of upgrade and update service. A cost analysis model has been proposed for the same.Design/methodology/approachThe article discusses a cost analysis model highlighting the distinction between launch time and time to end the testing process. The number of bugs which have to be catered in each release has been determined which also consists of the count of latent bugs of previous version. Convolution theory has been utilized to incorporate the joint role of tester and user in bug detection into the model. The cost incurred in debugging process was determined. An optimization model was designed which considers the reliability and budget constraints while minimizing the total debugging cost. This optimization was used to determine the release time and testing stop time.FindingsThe proposal is backed by real-life software bug dataset consisting of four releases. The model was able to successfully determine the ideal software release time and the testing stop time. An increased profit is generated by releasing the software earlier and continues testing long after its release.Originality/valueThe work contributes positively to the field by providing an effective optimization model, which was able to determine the economic benefit of the combined use of upgrade and update service. The model can be used by management to determine their timelines and cost that will be incurred depending on their product and available resources.

Epidemiology of skin diseases in the Tigray region of Ethiopia.

Background Skin diseases are very common among people living in poor countries. Although many of these pathologies might not be fatal, some can have a great impact on the patient, impairing their ability to work or worsening his/her relationship with the community. Understanding the epidemiology of skin diseases in these areas, determining the prevalence of different disorders, is fundamental to develop better educational and preventative programs. Methods We collected data from 467 consecutive patients referring to the Dermatology Center of the Axum Referral Hospital (Tigray region, Ethiopia). We investigated health status and environmental data. Diagnoses were classified into 6 groups (i.e. infectious, inflammatory, etc.). A statistical analysis was performed using IBM SPSSTM software version 25.0.1 (IBM SPSS Inc. Chicago, Illinois) and StataTM software release 14.2 (Stata Corporation, College Station, Texas). Normality of the distributions was assessed using the Kolmogorov-Smirnov test. Categorical variables are compared with the use of the Chi Square test or the Fisher's exact test, as appropriate. Results Inflammatory and infectious diseases were the most frequently observed. No significant differences in inferential tests between access to water, housing, education level, and any diagnoses group were found. Curiously, a statistically significant difference between inflammatory diseases and unemployment was found. Conclusions Easier access to medical care, medications, and clean water, together with a cleaner work and home environment, are the first goals to be achieved in order to decrease morbidity in these areas.

SERP4IoT'21 Workshop Report

We face a new software crisis. In 1968, computer scientists learned that developing robust software requires skills, methods, and tools. Today, software and hardware engineers realize that developing a robust Internet of Things (IoT) also pushes the states of their art and practice. Recent news illustrate the many problems faced by IoT: from lack of interoperability to broken updates to massive security attacks. In this context, the 3rd International Workshop on Software Engineering Research and Practices for the Internet of Things (SERP4IoT) aims to provide a highly interactive forum for researchers and practitioners to address the challenges of, nd solutions for, and share experiences with the development, release, and testing of robust software for IoT systems.

A Safe and Practical Cycle for Team-Based Development and Implementation of In-House Clinical Software

PurposeDue to a gap in published guidance, we describe our robust cycle of in-house clinical software development and implementation, which has been used for years to facilitate the safe treatment of all patients in our clinics. Methods and MaterialsOur software development and implementation cycle requires clarity in communication, clearly defined roles, thorough commissioning, and regular feedback. Cycle phases include design requirements and use cases, development, physics evaluation testing, clinical evaluation testing, and full clinical release. Software requirements, release notes, test suites, and a commissioning report are created and independently reviewed before clinical use. Software deemed to be high-risk, such as those that are writable to a database, incorporate the use of a formal, team-based hazard analysis. Incident learning is used to both guide initial development and improvements as well as to monitor the safe use of the software. ResultsOur standard process builds in transparency and establishes high expectations in the development and use of custom software to support patient care. Since moving to a commercial planning system platform in 2013, we have applied our team-based software release process to 16 programs related to scripting in the treatment planning system for the clinic. ConclusionsThe principles and methodology described here can be implemented in a range of practice settings regardless of whether or not dedicated resources are available for software development. In addition to teamwork with defined roles, documentation, and use of incident learning, we strongly recommend having a written policy on the process, using phased testing, and incorporating independent oversight and approval before use for patient care. This rigorous process ensures continuous monitoring for and mitigatation of any high risk hazards.

KM3NeT Acquisition Control: advanced techniques and best practices in data acquisition software development

The Control Unit of the KM3NeT Data Acquisition is the software suite that is responsible for operating all the components of the KM3NeT telescopes in a coordinated and scientifically proficient way. It controls a wide span of parameters and procedures, from the power supplies, to the operating voltages of more than 64000 photomultipliers in each detector block, to the setup of the various trigger algorithms that are applied online. The same software suite is also designed to be used in all test and qualification benches, from single Digital Optical Modules to full Detection Units. As the KM3NeT detectors are being incrementally built, the Control Unit is employed in a variety of setups and configurations, and is a dynamic software project, still adapting to shifting needs.The conflicting requirements of flexibility and stability are reconciled by proper code development policies. The Control Unit is able to cope with dynamically changing scenarios of multiple firmware generations coexisting in the same detector, for various reasons including hardware compatibility as well as testing purposes. The code also allows for static verification and extensive unit tests.A Central Logic Board Simulator software was also developed to help testing the whole architecture. Such a simulator provides properly faked slow control parameters, features a fully specification-compliant state machine and can generate fake data with specific profiles to feed the Trigger and Data Acquisition System. In this way, offline integration tests can be executed at each new software release, ensuring their smooth deployment to production sites and minimising chances of mistakes by operators.

A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines.

In recent years, the replicability of neuroimaging findings has become an important concern to the research community. Neuroimaging pipelines consist of myriad numerical procedures, which can have a cumulative effect on the accuracy of findings. To address this problem, we propose a method for simulating artificial lesions in the brain in order to estimate the sensitivity and specificity of lesion detection, using different automated corticometry pipelines. We have applied this method to different versions of two widely used neuroimaging pipelines (CIVET and FreeSurfer), in terms of coefficients of variation; sensitivity and specificity of detecting lesions in 4 different regions of interest in the cortex, while introducing variations to the lesion size, the blurring kernel used prior to statistical analyses, and different thickness metrics (in CIVET). These variations are tested in a between-subject design (in two random groups, with and without lesions, using T1-weigted MRIs of 152 individuals from the International Consortium of Brain Mapping (ICBM) dataset) and in a within-subject pre-/post-lesion design [using 21 T1-Weighted MRIs of a single adult individual, scanned in the Infant Brain Imaging Study (IBIS)]. The simulation method is sensitive to partial volume effect and lesion size. Comparisons between pipelines illustrate the ability of this method to uncover differences in sensitivity and specificity of lesion detection. We propose that this method be adopted in the workflow of software development and release.

Assessing the Effects of COVID-19 on Accommodations Availabilities and Prices

In 2020 a new pandemic, named COVID-19 has been spreading all over the world, causing a reduction of activities, including in the tourism sector. This paper tries to quantify the effects of COVID-19 on accommodations, with a particular focus on prices trend and accommodations availability. Experiments simulated more than 400 accommodation bookings over the period of time before, during and after the wave of the pandemic caused by COVID-19. The analysis is done for the city of Pisa, but it could be generalized to all the other cities, provided that there is an availability of data. The typology with the highest drop in availability was that of 2-star hotels with a maximum decrease of 66%. Even the 4 and 3-star hotels were clearly affected by the pandemic, recording maximum drops of 36% for 4-star hotels and 25% for 3-star hotels. Regarding the analysis of prices trend, the categories most affected by the pandemic were hotels, hostels and tourist villages, which recorded significant price increases. The major novelty of this paper involves the definition of a strategy which can be used to analyze the impact of COVID-19 on accommodations, as well as the release of the DOTApy software for the extraction of data. Doi: 10.28991/HIJ-2021-02-03-07 Full Text: PDF

Night Shift Work Accelerates Menopausal Age in Health Workers

Introduction: Earlier menopause has been associated with increased risk of cardiovascular disease, osteoporosis, shorter life expectancy and even cognitive decline. There are many factors that cause differences in the age of menopause in women, one of these environmental factors is a history of night shift work as a consequence of a job. It is not yet known whether female medical workers, with a history of night shifts, get earlier menopause.Methods: This case control study was conducted among 57 female night workers of the Dr. Soetomo General Academic Hospital. Data collection was conducted from December 2019 - March 2020. The study sample was postmenopausal health workers (nurse and midwife) and administrators. The instrument used in this study was a list of interview questions. The data was processed using SPSS software release 23.Results: The results show that from 45 children diagnosed with ALL, 53% are of the age ≤ 5 years old, with 58% males and 42% females. 13% of the patients are in the high risk group and 87% are in the standard risk group. Nutritional statuses of patients are 2% of them obese experienced remission after induction phase therapy, 56% normal with 80% of them experienced remission. 40% underweight with 89% of them experienced remission and 11% not experienced remission, 2% malnutrition and experienced remission. There is no correlation between the nutritional status of children with acute lymphoblastic leukemia with the outcome of induction phase (p = 0.798).Conclusion: In conclusion, there is no correlation between nutritional status and remission outcome of patients with ALL in the induction phase of therapy. However, high percentage of underweight patients shows nutrition needs special attention to improve therapy outcomes.

GNINA 1.0: molecular docking with deep learning

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

Change‐points‐based software scheduling

AbstractSoftware reliability literature consists of various change‐point‐based software reliability growth models and related release time problems. The primary assumption of the existing models is the existence of change‐point before software release time only. This does not look practical as the testing team becomes more proficient in detecting the faults due to their continuous involvement in software development by the software release time. Hence the fault detection rate in the pre‐ and postrelease phase is not the same. To capture this change in fault detection rate in the pre‐ and postrelease testing phase, we propose a new software reliability modeling framework by considering two change‐points during the software lifecycle; that is, there exists a change‐point before release time and release time as a change‐point. Further, in the last one‐decade software firms have changed their strategy of stop testing the software after release and continue to test even after release to remove the number of faults to provide better user experiences. This phenomenon attracted academicians to develop theoretical as well empirical study on postrelease testing and formulation of related release time problem. In this paper, we propose a software cost model to determine optimal release and testing stop time considering under the assumption of two change‐points as mentioned above. The proposed model is validated on real‐life data set.

Application of VR Technology in Jewelry Display

As a special symbolic cultural carrier that reflects people’s material life and spiritual state, jewelry plays an increasingly important role in life. How can we accelerate brand promotion, promote jewelry product sales, and establish a rapid market response mechanism? High efficiency, high quality, and low cost to meet consumers’ increasingly personalized and diversified needs are the problems we currently need to solve. The purpose of this article is to explore the application of VR technology in jewelry displays and provide a brand new idea for jewelry display. In order to realize the virtualization of the jewelry design process, this article uses the Cult 3D VR platform to complete the design and realization of the interactive function of the jewelry virtual model, uses the Photoshop software to design the jewelry virtual display system interface, and finally completes the jewelry virtual display system in the Dreamweaver software integration and release. Through detailed example application, the feasibility of the viewpoint of this subject was effectively verified. In this paper, the two algorithms BRSK and SURF are used in conjunction, and the multiscale expression characteristics of BRISK in space and the rotation-invariant characteristics of SURF are used. Studies have shown that the experimental results of the rotation performance of the method in this paper show that the accuracy is improved by 60%, and the time-consuming is relatively less. Therefore, under the premise of ensuring the rapidity, the method in this paper can guarantee the accuracy and time cost of control matching.

Managing Spare Parts Inventory by Incorporating Holding Costs and Storage Constraints

Abstract A key factor for motivating intending buyers of raw materials is vendor responsiveness. Therefore, to meet demand, a pre-approved level of stocks is often maintained. In contrast, the decision to keep an uncontrolled amount of stock could be counter-productive with cost components associated with holding often ignored unintentionally. In this study, the objective is to develop a spare parts inventory model that incorporates ignored holding costs with a storage constraint for a motorcycle assembly plant (MAP). The inventory policy, structure of holding costs, and spare parts sales reports were consulted for relevant data. The spare parts were categorized and selected using ABC analysis. A spare parts inventory model, which considers ignored holding cost, was formulated. The model was executed using Lingo optimisation software release 18.0.56 to determine the pair of the order quantity (Ɋ) and reorder point (Ɍ). 177 spare part items were identified using ABC analysis. The parts categorisation revealed that 21, 31, 125 part items belong to categories A, B, and C with 81, 15 and 4% of annual sales value, respectively. From category A, nine items contributed significantly to overall sales. The demand pattern for these items was probabilistic based on their coefficient of variation. The pair (Ɋ, Ɍ) for items N, Z, AY, K, AM, J, P, AL and AZ are (174,688), (71,147), (78,150), (86,163), (18,15), (88,170), (128,118), (33,43) and (87,152), respectively. These pairs yielded a total inventory cost of ₦2,177,363 when compared to the current total inventory investment of ₦6,800,000 resulting in a 67.9% cost reduction. A model to manage spare parts inventory with relevant holding cost components was developed for MAP to ensure the availability of items, maximize usage of storage space, and minimize total inventory cost.

Modul Front-End Sistem Informasi Geospasial Patroli Terpadu Kebakaran Hutan dan Lahan

To prevent and handle forest and land and forest fire (karhutla), the Ministry of Environment and Forestry assembled a patrol team that conducts a daily task to observe directly to the hotspot location as an indication for land fire. Currently, the patrol team reported the investigation result into a group chat. This method consumed many storage spaces and not suitable for formal reporting. This study aims to develop a front-end module for a web GIS application that visualizes the patrol team's daily report. The application has its data recapitulation method and able to create a formal report. The data used in this study are a set of the report that collected in 2016 by Sumatera and Kalimantan patrol team. The steps to build this application include communication, integrate with the API from the back-end system, developing functional needs, software testing, and the last is software release. The application was build using HTML and CSS for its interface and Javascript and API from the back-end module for its content management. The system uses Google Maps services and library to support the functionalities of the application. The unit testing method's test result shows that the module runs well and can afford all of the required functionality. In addition, the system testing result that the ratio between actual error and expected error is equal to 1. This result indicates the functions of the system are working properly according to the use cases of the system.

Continuously Managing NFRs: Opportunities and Challenges in Practice

Non-functional requirements (NFR), which include performance, availability, and maintainability, are vitally important to overall software quality. However, research has shown NFRs are, in practice, poorly defined and difficult to verify. Continuous software engineering practices, which extend agile practices, emphasize fast paced, automated, and rapid release of software that poses additional challenges to handling NFRs. In this multi-case study we empirically investigated how three organizations, for which NFRs are paramount to their business survival, manage NFRs in their continuous practices. We describe four practices these companies use to manage NFRs, such as offloading NFRs to cloud providers or the use of metrics and continuous monitoring, both of which enable almost real-time feedback on managing the NFRs. However, managing NFRs comes at a cost—as we also identified a number of challenges these organizations face while managing NFRs in their continuous software engineering practices. For example, the organizations in our study were able to realize an NFR by strategically and heavily investing in configuration management and infrastructure as code, in order to offload the responsibility of NFRs; however, this offloading implied potential loss of control. Our discussion and key research implications show the opportunities, trade-offs, and importance of the unique give-and-take relationship between continuous software engineering and NFRs. Research artifacts may be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://doi.org/10.5281/zenodo.3376342</uri> .

Multi-targeting approach for nsp3, nsp9, nsp12 and nsp15 proteins of SARS-CoV-2 by Diosmin as illustrated by molecular docking and molecular dynamics simulation methodologies.

Novel coronavirus SARS-CoV-2 continues to spread rapidly worldwide and causing serious health and economic loss. In the absence of any effective treatment, various in-silico approaches are being explored towards the therapeutic discovery against COVID-19. Targeting multiple key enzymes of SARS-CoV-2 with a single potential drug could be an important in-silico strategy to tackle the therapeutic emergency. A number of Food and Drug Administration (FDA) approved drugs entered into clinical stages were originated from multi-target approaches with an increased rate, 16–21% between 2015 and 2017. In this study, we selected an FDA-approved library (Prestwick Chemical Library of 1520 compounds) and implemented in-silico virtual screening against multiple protein targets of SARS-CoV-2 on the Glide module of Schrödinger software (release 2020-1). Compounds were analyzed for their docking scores and the top-ranked against each targeted protein were further subjected to Molecular Dynamics (MD) simulations to assess the binding stability of ligand–protein complexes. A multi-targeting approach was optimized that enabled the analysis of several compounds’ binding efficiency with more than one protein targets. It was demonstrated that Diosmin (6) showed the highest binding affinity towards multiple targets with binding free energy (kcal/mol) values of −63.39 (nsp3); −62.89 (nsp9); −31.23 (nsp12); and −65.58 (nsp15). Therefore, our results suggests that Diosmin (6) possesses multi-targeting capability, a potent inhibitor of various non-structural proteins of SARS-CoV-2, and thus it deserves further validation experiments before using as a therapeutic against COVID-19 disease.

An open-source python library for detection of known and novel Kell, Duffy and Kidd variants from exome sequencing.

Next generation sequencing (NGS) has promising applications in transfusion medicine. Exome sequencing (ES) is increasingly used in the clinical setting, and blood group interpretation is an additional value that could be extracted from existing data sets. We provide the first release of an open-source software tailored for this purpose and describe its validation with three blood group systems. The DTM-Tools algorithm was designed and used to analyse 1018 ES NGS files from the ClinSeq® cohort. Predictions were correlated with serology for 5 antigens in a subset of 108 blood samples. Discrepancies were investigated with alternative phenotyping and genotyping methods, including a long-read NGS platform. Of 116 genomic variants queried, those corresponding to 18 known KEL, FY and JK alleles were identified in this cohort. 596 additional exonic variants were identified KEL, ACKR1 and SLC14A1, including 58 predicted frameshifts. Software predictions were validated by serology in 108 participants; one case in the FY blood group and three cases in the JK blood group were discrepant. Investigation revealed that these discrepancies resulted from (1) clerical error, (2) serologic failure to detect weak antigenic expression and (3) a frameshift variant absent in blood group databases. DTM-Tools can be employed for rapid Kell, Duffy and Kidd blood group antigen prediction from existing ES data sets; for discrepancies detected in the validation data set, software predictions proved accurate. DTM-Tools is open-source and in continuous development.

SLAMcore announces new software release

SOFTWARE LOOKS TO ASSIST DEVELOPERS OF ROBOTS AND CONSUMER ELECTRONICS PRODUCTS. NEIL TYLER REPORTS

A Review of Malware Classification Methods using Machine Learning

This paper talks about methods, problems and solutions to Malware Classification using Machine Learning. It is believed that the amount of malicious software being released might surpass the release of authoritative software. Since malware gets more sophisticated every year, this results in a need to shift from traditional methods and make the system automatically learn. The main focus here is studying machine learning methods along with their problems for detection and classification. Feature selection and high false-positive problems are explained and solutions to them are presented. Opcode, n-gram opcode, image-based classification techniques and are then compared. These methods will help cleaning malware and to classify them into their families. The results based on accuracy are better when using n-gram opcode classification as compared to normal opcode and image-based classifiers but using an ensemble method combines the benefits of both methods like less overfitting and low FPR, eventually the results display increased classification accuracy and provide overall better classification of malware.

Congenital anomalies in children with postneonatally acquired cerebral palsy: an international data linkage study.

To describe the major congenital anomalies present in children with postneonatally acquired cerebral palsy (CP), and to compare clinical outcomes and cause of postneonatally acquired CP between children with and without anomalies. Data were linked between total population CP and congenital anomaly registers in five European and three Australian regions for children born 1991 to 2009 (n=468 children with postneonatally acquired CP; 255 males, 213 females). Data were pooled and children classified into mutually exclusive categories based on type of congenital anomaly. The proportion of children with congenital anomalies was calculated. Clinical outcomes and cause of postneonatally acquired CP were compared between children with and without anomalies. Major congenital anomalies were reported in 25.6% (95% confidence interval [CI] 21.7-29.9) of children with postneonatally acquired CP. Cardiac anomalies, often severe, were common and present in 14.5% of children with postneonatally acquired CP. Clinical outcomes were not more severe in children with congenital anomalies than those without anomalies. Cause of postneonatally acquired CP differed with the presence of congenital anomalies, with cerebrovascular accidents predominating in the anomaly group. Congenital anomalies were likely associated with cause of postneonatally acquired CP in 77% of children with anomalies. In this large, international study of children with postneonatally acquired CP, congenital anomalies (particularly cardiac anomalies) were common. Future research should determine specific causal pathways to postneonatally acquired CP that include congenital anomalies to identify opportunities for prevention. One-quarter of children with postneonatally acquired cerebral palsy (CP) have a major congenital anomaly. Cardiac anomalies, often severe, are the most common anomalies. Causes of postneonatally acquired CP differ between children with and without congenital anomalies.

Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators

Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms to solve NRP problem instances from four datasets. The cost-to-score ratio and the roulette wheel are used to satisfy constraints of the NRP problem. We compare obtained Pareto fronts based on eight quality indicators. In addition to four general multi-objective optimization quality indicators, the three aspects of fairness among clients and also uncertainty are reconfigured as quality indicators. These quality indicators are computed for a Pareto front. Results show that MOWOA performs better than others and makes requirement selection fairer. MOGWO works better than the rest when budget constraints are reduced.