Final Software Research Articles

Evaluation of guided reporting: quality and reading time of automatically generated radiology report in breast magnetic resonance imaging using a dedicated software solution.

Unstructured, free-text dictation (FT), the current standard in breast magnetic resonance imaging (MRI) reporting, is considered time-consuming and prone to error. The purpose of this study is to assess the usability and performance of a novel, software-based guided reporting (GR) strategy in breast MRI. Eighty examinations previously evaluated for a clinical indication (e.g., mass and focus/non-mass enhancement) with FT were reevaluated by three specialized radiologists using GR. Each radiologist had a different number of cases (R1, n = 24; R2, n = 20; R3, n = 36). Usability was assessed by subjective feedback, and quality was assessed by comparing the completeness of automatically generated GR reports with that of their FT counterparts. Errors in GR were categorized and analyzed for debugging with a final software version. Combined reading and reporting times and learning curves were analyzed. Usability was rated high by all readers. No non-sense, omission/commission, or translational errors were detected with the GR method. Spelling and grammar errors were observed in 3/80 patient reports (3.8%) with GR (exclusively in the discussion section) and in 36/80 patient reports (45%) with FT. Between FT and GR, 41 patient reports revealed no content differences, 33 revealed minor differences, and 6 revealed major differences that resulted in changes in treatment. The errors in all patient reports with major content differences were categorized as content omission errors caused by improper software operation (n = 2) or by missing content in software v. 0.8 displayable with v. 1.7 (n = 4). The mean combined reading and reporting time was 576 s (standard deviation: 327 s; min: 155 s; max: 1,517 s). The mean times for each reader were 485, 557, and 754 s, and the respective learning curves evaluated by regression models revealed statistically significant slopes (P = 0.002; P = 0.0002; P < 0.0001). Overall times were shorter compared with external references that used FT. The mean combined reading and reporting time of MRI examinations using FT was 1,043 s and decreased by 44.8% with GR. GR allows for complete reporting with minimized error rates and reduced combined reading and reporting times. The streamlining of the process (evidenced by lower reading times) for the readers in this study proves that GR can be learned quickly. Reducing reporting errors leads to fewer therapeutic faults and lawsuits against radiologists. It is known that delays in radiology reporting hinder early treatment and lead to poorer patient outcomes. While the number of scans and images per examination is continuously rising, staff shortages create a bottleneck in radiology departments. The IT-based GR method can be a major boon, improving radiologist efficiency, report quality, and the quality of simultaneously generated data.

An embedded TensorFlow lite model for classification of chip images with respect to chip morphology depending on varying feed

AbstractTurning is one of the fundamental machining processes used to produce superior machine parts. It is critical to manage the machining conditions to maintain the desired properties of the final product. Chip morphology and chip control are crucial factors to be monitored. In particular, the selection of an appropriate feed has one of the most significant effects. On the other hand, machine learning is an advanced approach that is continuously evolving and helping many industries. Moreover, mobile applications with learning models have been deployed in the field, recently. Taking these motivations into account, in this study, we propose a practical mobile application that includes an embedded learning model to provide chip classification based on chip morphology. For this purpose, a dataset of chips with different morphological properties is obtained and manually labeled according to ISO 3685 standards by using 20 different feeds on AISI 4140 material. Accordingly, TensorFlow Lite is used to train a learning model, and the model is embedded into a real-time Android mobile application. Eventually, the final software is evaluated through experiments conducted on the dataset and in the field, respectively. According to the evaluation results, it can be stated that the learning model is able to predict chip morphology with a test accuracy of 85.4%. Moreover, the findings obtained from the real-time mobile application satisfy the success rate by practical usage. As a result, it can be concluded that such attempts can be utilized in the turning process to adjust the relevant feed conditions.

Verb database: Structure, clusters and options

The content and volume of language corpora provide an opportunity to obtain reliable information about the real use of a particular linguistic unit. Nowadays, there is a large number of corpora in different languages, their formation technologies are being improved. Nevertheless, some problems and limitations arise when using these resources in comparative studies. Corpora users need to work with annotated data submitted to tagging through annotation protocols. The article presents the structure and functionality of the supracorpora verb database (SVD) developed on the basis of a parallel Russian-French subcorpus of the Russian National Corpus (RNC) and reveals the difference in their potentials. The described database is a pilot version of the final software, which is currently under development and is being tested. It consists of several clusters focused on solving such linguistic tasks as studying the grammatical semantics specifics and the distribution of verb forms in Russian and French; identifying the polysemantic structure in the two languages, which in turn verifies the understanding of the linguistic worldview of the speakers of Russian and French. It has been found that the mechanism of functioning of SVD cluster formations allows us to study both individual characteristics of verbs and the semantics of verbal lexemes and collocations. The manual annotation enables users to identify the systematic asymmetry of verb forms and cases of contextual and low-frequency asymmetry. Thus, SVD can be used in language pedagogy, teaching and studying discursive grammar, as well as the analysis of translation models variability.

Parametrized Optimization Based on an Investigation of Musical Similarities Using SPARK and Hadoop

The big data processing framework Spark is used to power a parameterizable recommender system that can make recommendations for music based on a user’s individual tastes and take into account a variety of musical tonal qualities. The system as it is presently built is completely scalable, which means that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio information, in addition to more cutting-edge similarity evaluations, may be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recommendations for a dataset including more than 114,000 songs may be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and similarity assessments. After the features have been retrieved, they are sent to the Spark-based recommender system to be processed. The calculated distance was displayed, examined, and graphically depicted. By computing the distance depending on the melody, rhythmic, and timbral components of the music, the final software controls song suggestion.

Abstract 080: Continuous Automated Stroke Screening Software for Early Detection of Neurological Impairment

Introduction Stroke is the second leading cause of death worldwide leaving up to 50 % of Survivor chronically disabled after their event. Early diagnosis and treatment can significantly lower mortality and morbidity, significantly reducing the economic burden of long‐term disability. Due to the painless nature of most stroke events, many lack the stimulus to seek emergency assistance, further compounded by a symptomatology of deficit resulting in being unaware of the symptoms or lacking the ability to call for help when needed. A continuous automated stroke screening software tool was developed to address these pitfalls in prehospital care, allowing for the early detection of neurological impairment and the release of a medical emergency alert to facilitate emergency medical care. Methods Python version 3.11.3, NumPy, OpenCV, and mediapipe were used for facial and hand land marking with mathematical models employed to detect eye gaze direction, facial symmetry, eyelid closure and left or right hand detection. Results We were able to demonstrate consistent performance of the final software to continuously detect the eye gaze deviation, or center in real time video capturing (Figure‐1). Software is also capable of detecting lower facial palsy through facial symmetry recognition of smiling to demonstrate a right or left sided palsy (Figure‐2). The ability to blink can be detected to differentiate motor neuron palsy and used as a measure of mental status demonstration of the ability to follow a simple midline command (Figure‐3). Detection of lateralizing hand presentation to the camera allows the software to be used in detecting hemi‐neglect, and antigravity muscle strength in upper extremity. (Figure 1‐3). Conclusion Our developed automated stroke screening software can be used for continuous, physician independent, monitoring of neurologic patients and the detection of acute deficits and emergency alert to facilitate early care. The software is designed for use in medical emergency alert systems, tele stroke assessments, and remote surveillance of the neurological examination in intensive care unit or patients in isolation. Here we present the initial software development and capability, we are currently studying our detection models on patients with neurological deficits in varied practice settings.

Using software metrics for predicting vulnerable classes in java and python based systems

ABSTRACT [Context:] Failure to predict vulnerability in the earlier stage of development can cause vulnerable code being written and deployed in the final software product. Vulnerability prediction using software metrics as features can support the discovery process by localizing vulnerable code. Existing studies have successfully employed metrics for vulnerability prediction for some platforms (C/C++ or Java projects). We propose that a comparative evaluation of how these metrics perform in projects of different languages can help the developers in deciding whether metrics-based prediction approach can be effective in their own project’s context. [Objective:] The purpose of this research is to analyze/compare the performance of software metrics in vulnerability-prediction for different programming language contexts (Java vs. Python). [Method:] We conducted experiments on vulnerabilities reported for Apache Tomcat (releases 6 and 7), Apache CXF, and two Python projects (Django and Keystone). We applied machine learning for predicting a particular type of code component (Java and Python classes) as vulnerable/non-vulnerable. [Results:] We found that metrics-based prediction can predict Java vulnerable classes with higher recall and precision than the Python vulnerable classes. [Conclusion:] This study at class-level will help developers to predict vulnerabilities at the class-level and assist in secure coding in object-oriented programming.

Gaussian kernelized feature selection and improved multilayer perceptive deep learning classifier for software fault prediction

Software fault prediction is the significant process of identifying the errors or defects or faults in a software product. But, accurate and timely detection is the major challenging issue in different existing approaches to predicting software defects. A novel Gaussian linear feature embedding-based statistical test piecewise multilayer perceptive deep learning classifier (GLFE-STPMPDLC) is introduced to improve software fault prediction accuracy and minimize time consumption. First, the input data are collected from the dataset. Next, the software metrics selection is carried out to select the significant metrics using Gaussian kernelized locally linear embedding with lesser software fault prediction. Then classification is carried out by Kaiser Meyer piecewise multilayer perceptive deep learning classifier for software fault prediction. The novelty of Kaiser–Meyer–Olkin (KMO) correlation test analyzes testing and training instances. The innovation of the Heaviside step activation function is applied for analyzing the KMO correlation test results and providing the final software fault prediction results. Finally, accurate fault prediction outcomes are achieved at the output layer with lesser error. Simulation of proposed GLFE-STPMPDLC technique achieves better 5%, 3%, 3% and 3% enhancement of fault prediction accuracy, precision, recall, and f-measure and 13% faster prediction time compared to conventional methods.

Forecasting the Principal of Code Technical Debt in JavaScript Applications

JavaScript (JS) is one of the most popular programming languages for developing client-side applications mainly due to allowing the adoption of different programming styles, not having strict syntax rules, and supporting a plethora of frameworks. The flexibility that the language provides may accelerate the development of application, but also pose threats to the quality of the final software product, e.g., introducing Technical Debt (TD). TD reflects the additional cost of software maintenance activities to implement new features, occurring due to poorly developed solutions. Being able to forecast the levels of TD in the future can be extremely valuable in managing TD, since it can contribute to informed decision making when designating future repayments and refactoring budget among a company's projects. Despite the popularity of JS and the undoubtful benefits of accurate TD forecasting, in the literature, there is available only a limited number of tools and methodologies that are able to: (a) forecast TD during software evolution, (b) provide a ground-truth TD quantifications to train forecasting, since TD tools that are available are based on different rulesets and none is recognized as a state-of-the-art solution, (c) take into consideration the language-specific characteristics of JS. As a main contribution for this study, we propose a methodology (along with a supporting tool) that supports the aforementioned goals based on the Backward Stepwise Regression and Auto-Regressive Integrated Moving Average (ARIMA). We evaluate the proposed approach through a case study on 19,636 releases of 105 open-source applications. The results point out that: (a) the proposed model can lead to an accurate prediction of TD, and (b) the Number of appearances of the “new” and “eval” keyword along with the number of “anonymous” and “arrow” functions are among the features of JavaScript language that are related to high levels of TD.

Development of Software Module for Recognizing Traffic Flows Through Deep Learning

This work describes a software system that uses machine learning methods and classifies vehicles observed in a frame. Analysis of the subject area and the possibilities provided by the methods of machine learning in the framework of the performance of assigned tasks are realized. An important part of the work is devoted to a study of the features of machine learning technologies with a teacher. In particular, the basic principles of the functioning of convolutional artificial neural networks are considered. This paper describes the implementation of the final software module, and its functionality, and demonstrates the results of its testing. Results from the developed software module show significant improvement in the accuracy over other methods, such as Artificial Neural Networks. Further developments of this technology may lead to broader applications and further improvements.

Software Reliability Growth Models with Exponentiated-gompertz Testing Effort and Release Time Determination

Quality is a consequential factor for the software product. During the software development at most care was taken at each step for the quality product. Development process generally embedded with several qualitative and quantitative techniques. The characteristics of final software product should reach all the standards. Reliability is a paramount element which quantifications the probability that a software product could able to work afore it authentically fails to perform its intended functionality. Software testing is paramount phase where gargantuan resources were consumed. Over around fifty percent of cost was consumed during this testing phase, that is why testing was performed in disciplined environment. Software product release time is considered to be crucial subject at which the software product testing was stopped and it could be release into market, such that the software product should have quality and reliability. In this paper we have investigated the concept of software testing effort dependent software reliability growth models by considering the exponentiated-gompertz function as testing effort function to determine the release time of the software. Thus, constructed testing effort dependent models was computed on three authentic time datasets. Parameter estimation is done through least square estimation and metrics like Mean square Error (MSE) and Absolute Error (AE) are utilized for model comparison. The proposed testing effort dependent model performance was better than the rest of the models.

Enriching UML Statecharts through a Metamodel: A Model Driven Approach for the Graphical Definition of DEVS Atomic Models

The Discrete Event System Specification (DEVS) formalism provides a set of mathematical elements for modeling time-varying systems. However, when DEVS models are implemented in an executable representation (i.e., using a general-purpose programming language), some deviation from the formalism is unavoidable. One way to bridge the gap between modeling and simulation theory and practice is to define new artifacts that support both views during the specification. When the specification is supported with a graphical representation, the formalization task is less complex and can be performed by non-expert modelers. For DEVS atomic models, most common graphical representation is through UML statecharts. In this paper, we present a theoretical and practical metamodel for the definition of atomic models structured following the Classic DEVS with Ports formalization. Such a metamodel is the core of a model-driven approach used to develop a modeling software tool that employs enriched UML statecharts for the graphical representation of the DEVS behavior. In here, the traditional UML statechart representation is enriched with a set of new components with the aim to provide a broad definition of DEVS atomic models. The final software tool is deployed as a plugin for Eclipse Platform.

The Autonomous Train vision: Embedded AI for pedestrians monitoring

When you develop a perception system for rolling stocks of the railway sector, you are dealing with huge infrastructures and harsh conditions never encountered. From the integration studies to the final software implementation of the computer vision algorithm in the embedded system, our work puts on paper these steps and challenges encountered by focusing on one specific use-case of the environment monitoring of a train: pedestrians tracking in front of the train. Pedestrian monitoring is a key aspect of autonomous train operation as it involves ensuring the safety of people in the vicinity of the tracks. This scenario covers most of the requirements for automatic detection scenarios at the front of a train: close and distant moving object detection, robust object tracking, person state analysis with high confidence rates to ensure safety for human life and for train driving.

Business Intelligence’s Self-Service Tools Evaluation

The software selection process in the context of a big company is not an easy task. In the Business Intelligence area, this decision is critical, since the resources needed to implement the tool are huge and imply the participation of all organization actors. We propose to adopt the systemic quality model to perform a neutral comparison between four business intelligence self-service tools. To assess the quality, we consider eight characteristics and eighty-two metrics. We built a methodology to evaluate self-service BI tools, adapting the systemic quality model. As an example, we evaluated four tools that were selected from all business intelligence platforms, following a rigorous methodology. Through the assessment, we obtained two tools with the maximum quality level. To obtain the differences between them, we were more restrictive increasing the level of satisfaction. Finally, we got a unique tool with the maximum quality level, while the other one was rejected according to the rules established in the methodology. The methodology works well for this type of software, helping in the detailed analysis and neutral selection of the final software to be used for the implementation.

Building a pervasive social gaming experience using SocialPG

AbstractPervasive computing has become a key element to build applications that use fun as a motivating component because it allows exploring new interaction schemes by making the concept of space and time ambiguous and confusing. The present research describes a pervasive social gaming experience, using as a reference SocialPG, which is a model that describes social expansion as a strategy to improve gaming experiences supported by pervasive computing. In this article a description of the model is offered, software architecture is proposed to support it and a case study related to the process of error resolution and detection of improvement opportunities in software products is developed, finally, a general idea about the final software product that will support the game experience is offered together with an evaluation performed by a set of users, where some important findings are highlighted, such as the importance of the missions as a unit of cooperative work and the spectator's participation.

Computer modeling of the tournament of game algorithms in the process of learning of basics of algorithmization and programming by pre-service IT-specialists

The problems of contemporary IT specialists’ training in terms of the high requirements to their computational thinking skills as well as the urgency of raising their motivation to mastering algorithmization and programming are discussed in the paper. It is emphasized by the authors that initial university courses should focus pre-service IT-specialists on the deep understanding of an algorithmic nature of any coding task, to realize basic characteristics of the algorithms, to understand their role in modern software development. Due to the contemporary demands, programming should rest on algorithms building and has to be a part of lager scale experiences in order to realize its full potential. One of such experiences offered by the authors in the paper is involving the students into specially arranged activity focused on efficient game algorithms creation and simulation of the tournament between the algorithms. The offered activity is elaborated based on the applying the gamification elements into the learning process. Basing on the core gamifiaction principles, there were thought over and arranged an activity involving the students into the creation of gamified products. In our case, the gamified product which the students had to develop in the process of learning of algorithmization and programming was the software platform which enables a computer simulation of the tournament between the different game algorithms which realize winning strategies. The peculiarities and the stages of the said activity are covered in details along with the description of the final software product. Analyzing the described functionality of the computer simulator of the algorithms tournaments based on the gamification ideas, we can emphasize its significant didactic facilities in the context of its using for IT-specialists training. In particular, the developed gamified product was probed in the process of other students’ mastering algorithmization and programming as well as of the schoolchildren training during summer IT schools. The prospects of the research are outlined in the lines of using the obtained results for holding the empirical research for the verification of offered activity impact on the results of IT-specialists training.

B model quality assessments on automated reachability repair with ISO/IEC 25010

In software engineering, formal methods are often used to specify and verify design models of software products. Whether design models are consistent with required properties can significantly impact the quality of final software products. In this work, we study B model quality measurements based on the ISO/IEC 25010 standard. These measurements are formulated as domain-independent formulae and computed by model checking. Moreover, we study how to enable machines to automatically solve unreachable goals in B models. We suggest to use constraint solvers and semantic learners to discover state transitions to the goals. To demonstrate the effectiveness of the model repair technique, a set of experiments are conducted based on the model quality measurements. The results demonstrate that the model repair technique can solve unreachable goals while preserving the quality of models. • ISO/IEC 25010 can be used to evaluate the quality of state spaces. • Twenty criteria for measuring the quality of abstract machines are proposed. • The reachability of abstract machines can be automatically repaired. • The quality of reachability repair can be evaluated using ISO/IEC 25010.

Strategies to Evolve ExM Notations Extracted from a Survey with Software Engineering Professionals Perspective

Contemporary complex systems often exhibit dynamic structures and behaviors, several components/systems involved, and multiple interoperability links. Those systems have been exposed to fragilities of traditional software specification languages (e.g. UML and SySML), since such languages were designed to document single (not multiple interoperating) systems. Those limitations can potentially further compromise the quality of the final software product. In this context, Executable Models (ExM) technology, such as simulation models, models@runtime and executable UML, satisfy the aforementioned requirements by supporting engineers with visualization of the system structures (still at design-time) and the ability to exercise their behaviors and interactions. In our prior study, we presented the results of an exploratory study on the perceptions of those professionals (from both industry and academia) regarding the use of ExM to solve problems in their current practice. We exposed 58 professionals (researchers and practitioners) to situations to solve problems using a specific type of ExM (DEVS simulation models), based on survey research. Responses were quantitatively and qualitatively analyzed. In this article, we extended the obtained results by analyzing and compiling a list of strategies to improve ExM notations to better address the needs of software engineering professionals. Later, we assessed those strategies with software engineering researchers to confirm the importance of the proposed strategies. Results revealed that executable languages still require advances to bring them even closer to the current software engineering practice and towards a more significant adoption in the future. The proposed strategies focus on improvements on the robustness of the ExM notations, visual representation of the models, the usability of the models, and user support.

Effective ANN Model based on Neuro-Evolution Mechanism for Realistic Software Estimates in the Early Phase of Software Development

There is no doubt that the software industry is one of the fastest-growing sectors on the planet today. As the cost of the entire development process continues to rise, an effective mechanism is needed to estimate the required development cost to control better the cost overrun problem and make the final software product more competitive. However, in the early stages of planning, the project managers have difficulty estimating the realistic value of the effort and cost required to execute development activities. Software evaluation prior to development can minimize risk and upsurge project success rates. Many techniques have been suggested and employed for cost estimation. However, computations based on several of these techniques show that the estimation of development effort and cost vary, which may cause problems for software industries in allocating overall resources costs. The proposed research study proposes the artificial neural network (ANN) based Neural-Evolution technique to provide more realistic software estimates in the early stages of development. The proposed model uses the advantages of the topology augmentation using an evolutionary algorithm to automate and achieve optimality in ANN construction and training. Based on the results and performance analysis, it is observed that software effort prediction using the proposed approach is more accurate and better than other existing approaches.

АНАЛІЗ ІНСТРУМЕНТІВ МЕТАПРОГРАМУВАННЯ В МОВАХ ПРОГРАМУВАННЯ ЗАГАЛЬНОГО ПРИЗНАЧЕННЯ

Modern methods of metaprogramming used in general-purpose languages are considered and analyzed. The metaprogramming approach is used for many scenarios of working with program code, but one of them is source code generation. Each programming language includes a unique set of tools for solving metaprogramming tasks. One of the methods of metaprogramming is the construction of annotation processors, however, they do not define the specifics of code generation. Another method is writing compiler plugins, which is possible in programming languages like Kotlin. The plugin interface can provide access to many stages of program compilation, but their disadvantage is the increased complexity of programs, which significantly affects the speed of development and the performance of the resulting software. Technologies such as KotlinPoet and JavaPoet allow generating the text of source programs with partial type safety at the level of statements. The disadvantage of such technologies is the significant difference between the code of the program that performs the generation and the original generated code, which increases the complexity of working with this tool and the overall cognitive load. In contrast to them, it is possible to use strings with an interpolation mechanism that provides declarativeness, but at the cost of type validation. Programming languages such as MetaOCaml and Scala implement mechanisms of metaprogramming and multi-stage programming, in particular, at the syntax level using quoting and splicing constructions and mechanisms of inlining and macros. Macros are also present in the C and C++ languages, they allow preprocessor directives to perform pre-transformation of the code before the main stage of compilation. The correct combination of metaprogramming elements will allow creating a universal approach to the use of a declarative metaprogramming functionality, which will provide a powerful toolkit for scaling the amount of generated code and increase the quality of the final software product.

Agile Software Development Techniques: A Survey

In this IT era, where there is a race of software development, it is necessary to introduce such types of software development techniques which will help the practitioners to deliver fast solutions. In the past, various traditional approaches were used for this purpose, but now agile techniques are getting more popular because conventional software development approaches are not efficient in managing the changing requirements. The agile software development process is one of the most emerging lightweight software development methodologies, which uses iterative and prototype development approaches to accommodate changes in software requirements. Final software products are delivered to the end-users in short iterations. One of the most noticeable drawbacks of agile methods is their limited courtesy to the structured and architectural design of the system. Hence this development approach will restrict small to medium design decisions only. In this paper, we have performed the analysis of different agile techniques, which will help the readers to understand their positive and negative points and select the most appropriate technique suited to their projects.