Software Development Problems Research Articles

Mobile application development for watching videos offline from YouTube in language C#

This article dedicated of mobile application software development problem for watching videos offline from YouTube. Relevance of this research is confirmed by the rapid growth in numbers of users to mobile applications, among the categories of which are the most popular games, photos and videos. Especially it was noticed for the last year due to coronavirus pandemic worldwide. Everyone understands that there will be no return to the previous life, there is transition to mobile-oriented existence. At work committed analysis of analogues mobile applications, their characteristics are presented and this let improve algorithm of speed download media files about 2 times compared to ssyoutube.com and presaver.com and simplify routing for user convenience. The functionality of the application is described in the form of a UML diagram, which shows a general idea of the functional purpose of our system, namely: the association between the actor and the use case, as well as the dependencies of different types between use cases. The study used modern software development technologies, in particular: the choice of Visual Studio development environment and C # programming language was substantiated, SQLite was taken as a database, and the LibVLC library was used to play videos in the application environment. Testing of this application was done by analysis of boundary conditions, stochastic, route and data processing. Mobile application is available for OS Android, allows you to download any video files from YouTube platform and to watch them offline when there is no access to the Internet network. Also this application has function which helps to add video to playlist for creating a collection of video files on the relevant topic or taste. Minimal routing is an advantage of this system, as the small number of pages and simple interface allow you to quickly navigate in this application.

On (Mis)perceptions of testing effectiveness: an empirical study

A recurring problem in software development is incorrect decision making on the techniques, methods and tools to be used. Mostly, these decisions are based on developers’ perceptions about them. A factor influencing people’s perceptions is past experience, but it is not the only one. In this research, we aim to discover how well the perceptions of the defect detection effectiveness of different techniques match their real effectiveness in the absence of prior experience. To do this, we conduct an empirical study plus a replication. During the original study, we conduct a controlled experiment with students applying two testing techniques and a code review technique. At the end of the experiment, they take a survey to find out which technique they perceive to be most effective. The results show that participants’ perceptions are wrong and that this mismatch is costly in terms of quality. In order to gain further insight into the results, we replicate the controlled experiment and extend the survey to include questions about participants’ opinions on the techniques and programs. The results of the replicated study confirm the findings of the original study and suggest that participants’ perceptions might be based not on their opinions about complexity or preferences for techniques but on how well they think that they have applied the techniques.

The Application Of Machine Learning In Test Case Prioritization - A Review

Regression testing is very important but also a very costly and time-consuming activity that ensures the developers that changes in the application will not bring new errors. Retest all, selection of test cases and prioritization of test cases (TCP) approaches are used to enhance the efficiency and effectiveness in regression testing. While test case selection techniques decrease testing time and cost, it can exclude some critical test cases that can detect the faults. On the other hand, test case prioritization considers all test cases and execute them until resources are exhausted or all test cases are executed, while always focusing on the most important ones. Over the years, machine learning has found wide usage in solving different problems in software engineering. Software development and maintenance problems can be defined as learning problems and machine learning techniques have shown to be very effective in solving these problems. In the range of application of machine learning, machine learning techniques have also found usage in solving the test case prioritization problem. In this paper, we investigate the application of machine learning techniques in test case prioritization. We survey some of the most recent studies made in this field and provide information like techniques of machine learning used in TCP process, metrics used to measure the effectiveness of the proposed methods, data used to define the priority of test cases and some advantages or limitations of application of machine learning in TCP.

A MULTIPLE NON-LINEAR REGRESSION MODEL TO ESTIMATE THE AGILE TESTING EFFORTS FOR SMALL WEB PROJECTS

Context. Software testing effort estimation is one of the important problems in software development and software testing life cycle. The object of the study is the process of estimating the agile testing efforts for small Web projects. The subject of the study is the multiple regression models for estimating the agile testing efforts for small Web projects. Objective. The goal of the work is the creation of the multiple non-linear regression model for estimating the agile testing efforts for small Web projects on the basis of the Johnson multivariate normalizing transformation. Method. The model, confidence and prediction intervals of multiple non-linear regression for estimating the agile testing efforts for small Web projects are constructed on the basis of the Johnson multivariate normalizing transformation for non-Gaussian data with the help of appropriate techniques. The techniques based on the multiple non-linear regression analysis using the multivariate normalizing transformations to build the models, equations, confidence and prediction intervals of multiple non-linear regressions are used. The techniques allow to take into account the correlation between random variables in the case of normalization of multivariate non-Gaussian data. In general, this leads to a reduction of the mean magnitude of relative error, the widths of the confidence and prediction intervals in comparison with the linear models and nonlinear models constructed using univariate normalizing transformations. Results. Comparison of the constructed model with the linear model and non-linear regression models based on the decimal logarithm and the Johnson univariate transformation has been performed. Conclusions. The multiple non-linear regression model to estimate the agile testing efforts for small Web projects is firstly constructed on the basis of the Johnson multivariate transformation for SB family. This model, in comparison with other regression models (both linear and non-linear), has a smaller value of the mean magnitude of relative error, smaller widths of the confidence and prediction intervals. The prospects for further research may include the application of other multivariate normalizing transformations and data sets to construct the multiple non-linear regression model for estimating the agile testing efforts for small Web projects.

Searching for Optimal Models: Comparing Two Encoding Approaches.

Search-Based Software Engineering (SBSE) is about solving software development problems by formulating them as optimization problems. In the last years, combining SBSE and Model-Driven Engineering (MDE), where models and model transformations are treated as key artifacts in the development of complex systems, has become increasingly popular. While search-based techniques have often successfully been applied to tackle MDE problems, a recent line of research investigates how a model-driven design can make optimization more easily accessible to a wider audience. In previous model-driven optimization efforts, a major design decision concerns the way in which solutions are encoded. Two main options have been explored: a model-based encoding representing candidate solutions as models, and a rule-based encoding representing them as sequences of transformation rule applications. While both encodings have been applied to different use cases, no study has yet compared them systematically. To close this gap, we evaluate both approaches on a common set of optimization problems, investigating their impact on the optimization performance. Additionally, we discuss their differences, strengths, and weaknesses laying the foundation for a knowledgeable choice of the right encoding for the right problem.

Recording Program of Basic System for Seismometric Monitoring of the Technical Condition of Buildings and Structures

Problems of software development for basic seismometric monitoring of the technical condition of buildings and structures are considered. A client–server architecture is chosen for the recording program of the system. The server component provides interconnection with the equipment, data acquisition, storage, and processing. The client component provides the user (operator) interface, hardware configuration and application, and representation of system tasks and results in user-friendly form. The seismic monitoring system operates in two modes: the main one includes the isolation and recording of seismic events with assessment of their impact on a structure using the MSK-64 scale; the advanced mode provides recording of microseismic vibrations according to the schedule (specified by the operator) for monitoring the technical condition of a structure. Moreover, the system continuously records oscillations of an object. Thus, the server program writes and stores three types of files: seismic events, monitoring, and continuous recording. When a seismic event is detected or system malfunctions arise, responsible personnel are notified by e-mail and SMS according to an approved list. All operator actions, as well as system functioning events, are fixed in the message log and system log. The client program provides visualization of data on oscillations at observation sites in the form of oscillation seismograms, current signal-amplitude spectra, and vibration-level histograms from the measurement channels for a selected component. All information important for the operator (display of sensors and their status, current configuration, seismograms of corresponding measurement channels, and a list of latest messages in the system) is presented in a mnemonic diagram, which is one of the main working windows of the application. The main specific features of the program are support of different types of equipment (seismic stations), independence from the number of connected seismic stations/recorders, wide possibilities in configuring the system hardware and the application itself, the possibility of unlimited user (client) connections for browsing seismometric data, and multilanguage support.

A Practical Approach for Evaluating and Prioritizing Situational Factors in Global Software Project Development

There has been an enormous increase in globaliza-tion that has led to more cooperation and competition across boundaries. Software engineering, particularly distributed soft-ware development (DSD) and global software development (GSD), is evolving rapidly and presents several challenges, such as ge-ographical separations, temporal differences, cultural variations, and management strategies. As a result, a variety of situational factors (SFs) arise that causes challenging problems in software development. Both literature and real world software industry study revealed that the extent of the effect of SFs may vary subject to a certain software project. Project executives should need to concentrate on the right SFs for the successful development of a specific project. This work first examines the optimal and most well-balanced GSD-related SFs and then presents a mechanism for prioritizing the SFs to better understand the extent to which an SF generally affects the GSD. A set of 56 SFs in 11 categories is identified and analyzed in this research. A fuzzy set theory based, multi criteria decision making (MCDM) technique, fuzzy analytical hierarchy process (FAHP) was proposed to extract the SFs that have the strongest effects on GSD. The proposed technique is intelligent and automated and can be customized to suit specific conditions and environments. Thus, it can provide support for a much-needed variation that is the hallmark of such software development environments. A case study of a global company working in collaboration on a project JKL was selected to identify and prioritize the most challenging SFs. A sensitivity analysis is carried out to evaluate the extent of the impact for highly ranked SFs related to JKL project.

A Critical Analysis of Software Risk Management Techniques in Large Scale Systems

Researchers in the software industry have focused on risk management systems for a long time. Software risk management is a software engineering practice that contains risk identification, risk estimation, mitigation, and monitoring. It delivers a disciplined environment for efficient decision-making to assess the problems in software development. Measuring risks in a large-scale system is comparatively difficult because of its complex nature. Large-scale systems are challenging since many risks can arise during system development. Risk factors in large-scale systems are relatively different from small systems, especially with respect to the independent components. This paper describes a difference between large-scale and small-scale systems along with an exhaustive list of risk factors. The tools from the literature are further divided into sub categories according to those that are best suited. We present a detailed comparative analysis for different software-related risk management models with some commonly identified features and further categorize them into classes based on the severity of their risks.

랜들리 및 어랑 수명분포에 의존한 소프트웨어 신뢰성 모형에 대한 신뢰도 속성 비교 연구

소프트웨어 개발시행 과정에서 소프트웨어 신뢰성은 매우 기본적이고 필수적인 문제 중에 하나이다. 소프트웨어 고장현상을 파악하기 위하여 비동질적인 포아송 과정에서 순간 고장률인 강도함수가 고장시간에 독립적으로 일정하거나, 증가형 혹은, 감소형 추세를 가질 수 있다. 본 논문에서는 소프트웨어 설계 과정에서 강도형태가 감소패턴을 따르는 랜들리 수명분포와 증가하다가 감소하는 어랑수명 분포를 활용한 소프트웨어 신뢰속성 모형에 대하여 신뢰도 장단점에 관한 연구를 하였다. 소프트웨어 고장현상을 파악하기 위하여 모수추정은 최우추정법을 적용하였다. 따라서, 본 논문에서는 소프트웨어 고장시간 자료를 적용하여 소프트웨어 신뢰도를 비교하고, 평가하였다. 그 결과, 랜들리 모형이 어랑분포 모형보다 신뢰도가 상승하는 것으로 나타났으나, 어랑분포 모형에서는 형상모수가 높을수록 높은 신뢰도를 나타내는 추세를 보였다. 본 논문를 통하여 소프트웨어 기획 부서에서는 특정한 수명분포와 형상모수를 활용함으로서 소프트웨어 고장분석을 활용한 소프트웨어 신뢰성 모형에 대한 신뢰성 속성을 적용한 데이터 및 기본 지식을 제공함으로서 소프트웨어 설계에 실질적인 도움을 줄 수 있다. Software reliability is one of the most basic and essential problems in software development. In order to detect the software failure phenomenon, the intensity function, which is the instantaneous failure rate in the non-homogeneous Poisson process, can have the property that it is constant, non-increasing or non-decreasing independently at the failure time. In this study, was compared the reliability performance of the software reliability model using the Landely lifetime distribution with the intensity function decreasing pattern and Erlang lifetime distribution from increasing to decreasing pattern in the software product testing process. In order to identify the software failure phenomenon, the parametric estimation was applied to the maximum likelihood estimation method. Therefore, in this paper, was compared and evaluated software reliability using software failure interval time data. As a result, the reliability of the Landely model is higher than that of the Erlang distribution model. But, in the Erlang distribution model, the higher the shape parameter, the higher the reliability. Through this study, the software design department will be able to help the software design by applying various life distribution and shape parameters, and providing software reliability attributes data and basic knowledge to software reliability model using software failure analysis.

Understanding metric-based detectable smells in Python software: A comparative study

ContextCode smells are supposed to cause potential comprehension and maintenance problems in software development. Although code smells are studied in many languages, e.g. Java and C#, there is a lack of technique or tool support addressing code smells in Python. ObjectiveDue to the great differences between Python and static languages, the goal of this study is to define and detect code smells in Python programs and to explore the effects of Python smells on software maintainability. MethodIn this paper, we introduced ten code smells and established a metric-based detection method with three different filtering strategies to specify metric thresholds (Experience-Based Strategy, Statistics-Based Strategy, and Tuning Machine Strategy). Then, we performed a comparative study to investigate how three detection strategies perform in detecting Python smells and how these smells affect software maintainability with different detection strategies. This study utilized a corpus of 106 Python projects with most stars on GitHub. ResultsThe results showed that: (1) the metric-based detection approach performs well in detecting Python smells and Tuning Machine Strategy achieves the best accuracy; (2) the three detection strategies discover some different smell occurrences, and Long Parameter List and Long Method are more prevalent than other smells; (3) several kinds of code smells are more significantly related to changes or faults in Python modules. ConclusionThese findings reveal the key features of Python smells and also provide a guideline for the choice of detection strategy in detecting and analyzing Python smells.

Beyond Related Experience: Upstream vs. Downstream Experience in Innovation Contest Platforms with Interdependent Problem Domains

Problem definition: Innovation contest platforms are often organized around specific fields and host contests that span a variety of interdependent problem domains. Whereas contestants may benefit from related experience in contests whose problem domains share an interdependency with the focal problem domain, it is unclear whether the benefits of related experience arise symmetrically from upstream experience (i.e., experience in problem domains that provide input information to the focal problem domain) and downstream experience (i.e., experience in problem domains that use output information from the focal problem domain) or differ among them. Academic/practical relevance: Given that innovation contest platforms serve to effectively match contest problem requirements with contestants’ skills, it is important to understand how a contestant’s prior experience on a platform contributes to her problem-solving performance. Our research provides a more granular examination of the benefits of related experience than what has been examined in prior studies on individual learning or innovation contests. Methodology: We collected detailed archival data from TopCoder, a leading innovation contest platform that hosts contests across multiple interdependent software development problem domains, from its launch in 2001 to September 2013. Our data set comprises detailed participation histories of 821 contestants in 3,274 contests across eight interdependent problem domains involving 8,985 observations. Results: Whereas a contestant’s related experience on the innovation contest platform is more positively associated with her focal contest performance compared with unrelated experience, the benefits of related experience arise only from downstream experience. That is, there are no significant performance benefits of upstream experience. Furthermore, the performance benefits of downstream experience are greater when the contest duration is shorter, highlighting its role in enabling more efficient search and problem solving in innovation contest platforms with interdependent problem domains. Managerial implications: Contrary to the notion of “hyperspecialization,” our findings suggest that contestants can reap benefits from diversifying their experience into downstream problem domains on innovation contest platforms. Furthermore, innovation contest platforms could facilitate such targeted diversification of contestant experience by developing more granular metrics of contestant experience across problem domains. Our findings also have implications for resource allocation and job rotation decisions in software development projects within firms.

ALGORITHMS OF CRESCENT STRUCTURE DETECTION IN HUMAN BIOLOGICAL FLUID FACIES

Abstract. One of the effective methods of early medical diagnosis is based on the image analysis of human biological fluids. In the process of fluid crystallization there appear characteristic patterns (markers) in the resulting layer (facies). Each marker is a highly probable sign of some pathology even at an early stage of a disease development. When mass health examination is carried out, it is necessary to analyze a large number of images. That is why, the problem of algorithm and software development for automated processing of images is rather urgent nowadays. This paper presents algorithms to detect a crescent structures in images of blood serum and cervical mucus facies. Such a marker indicates the symptoms of ischemic disease. The algorithm presented detects this marker with high probability when the probability of false alarm is low.

Development of the mobile software application for determination of optimal air ionisation modes

The paper deals with the problem of software development that solves the problems connected with creation of the optimum aerial ionization mode on a poultry farm, namely: creation of geometrical model which depends on parameters of the room and the ionizer power and output of graphic results to the screen.

Markers detection on facies of human biological fluids

The precise diagnostics of different diseases is very important for its treatment. It is particularly important to differentiate the disease on the early stages when the pathological alterations have not yet caused great harm to the whole organism, since this allows using greater number of therapies and increase the recovery probability. One of the methods of early diagnostics is based on examination of human biological liquids (blood, tears, cervical mucus, urine, etc,). A small drop of liquid is drawn on an object-plate and dried out slowly, thus a thin dry film (facies) remains. In the process of fluid crystallization there appear characteristic patterns (markers) in the facies. Each marker is a highly definite sign of some pathology even at an early stage of a disease development. When mass population health examination is carried out, it is necessary to analyze a large number of images This is the reason the problem of algorithm and software development for automated processing of images is rather urgent nowadays. In this paper, the algorithms for detecting several markers on images of facies are presented. First, the characteristic features (location, geometry, brightness, variation, spectrum, etc.) were revealed by means of their visual analysis of markers. Then, the methods of algorithmic detection of these features were developed. The decision about the presence of the marker was made provided a set of its necessary characteristics. Tests of algorithms showed that correctly identified images with different markers was 92-98% with 10-14% of false alarms.

Parallel Performance Problems on Shared-Memory Multicore Systems: Taxonomy and Observation

The shift towards multicore processing has led to a much wider population of developers being faced with the challenge of exploiting parallel cores to improve software performance. Debugging and optimizing parallel programs is a complex and demanding task. Tools which support development of parallel programs should provide salient information to allow programmers of multicore systems to diagnose and distinguish performance problems. Appropriate design of such tools requires a systematic analysis of the problems which might be identified, and the information used to diagnose them. Building on the literature, we put forward a potential taxonomy of parallel performance problems, and an observational model which links measurable performance data to these problems. We present a validation of this model carried out with parallel programming experts, identifying areas of agreement and disagreement. This is accompanied with a survey of the prevalence of these problems in software development. From this we can identify contentious areas worthy of further exploration, as well as those with high prevalence and strong agreement, which are natural candidates for initial moves towards better tool support.

Development of software and hardware system circuit for the computerization of physical experiments

The problem of computerization of laboratory experiments and technical implementation of the previously proposed software and hardware system circuit to solve it were considered. The existing range of systems can not completely solve the problem because of the complexity and expensiveness of equipment. Therefore, the problem of software and hardware system development for the laboratory experiment automation with minimum time and financial costs is relevant in our time. The central module of the system is the data reception, processing and transmission module. To create the system prototype, the task to calculate the input digital-to-analog paths of the data reception and transmission module was set. The calculation technique of electrotechnical circuits, allowing to obtain accurate values of electronic components, providing the necessary technical characteristics of the system as a whole was presented. The measurement accuracy is 1%, the input signal sampling frequency is 2000 samples per second per channel, the gain range is 1...1000, protection of input circuits from the high input voltage is up to 150V.

Software development and management problem analysis of investment portfolio structure

The problem of investors in the modern Ukrainian stock market is considered. Each investor faces a dilemma, wishing to have more yield and less risk. Since this task is controversial, the investor must make a compromise choice between yield and risk. The problem is that to be successful an investor needs help to assess the overall situation in the market. So far, there are so many online agencies, specialized in training to work with the securities market, but there is no the software that would allow to conduct investment portfolio management based on various mathematical models. Many people are involved in this area of the market, therefore; there is the need to automate securities transactions. It was proposed to consider methods for finding the risky structure of the portfolio and the allocation of available capital between the risky and nonrisky part of the portfolio using Markowitz models and Pontryagin’s max i mum principle, with the aim to develop an optimal reallocation strategy of investor funds. In carrying out this work, risky portfolio structure, found by maximizing the Sharpe relationship and based on the Markowitz portfolio theory, namely, the search for the minimum risk portfolio was determined. During the capital allocation between risky and nonrisky part of the portfolio, management in the form of feedback was used. All the cal­culations were made using the developed software and have allowed to evaluate the usefulness of the work. Research results and developed software can be used in investment departments of commercial banks and by small investors.

Toward Meta-Software Engineering Multistage Process

Information systems are being used for more and more applications are becoming more complicated and expensive, while at the same hand today’s software systems demand increasing tremendously for sophisticated software engineering processes. There are many ways to solve a single problem in software development. Sometimes, in Structural engineering, developer is not able to decide which process will suit to that particular problem or in other words we can say that selecting a good process is a big issue in Structural Process Software engineering. The solution of such kind of problem can be found in the work to be done, and the task to be performed by the Operational process rather than structure of a process. This paper introduces the notion of process operationality and proposes ‘process architecture’ to represent this operationality. Thus, Structural Process Engineering (SPE) becomes Operational Process Engineering (OPE).

PHandler: An expert system for a scalable software requirements prioritization process

Software requirements engineering is a critical discipline in the software development life cycle. The major problem in software development is the selection and prioritization of the requirements in order to develop a system of high quality. This research analyzes the issues associated with existing software requirement prioritization techniques. One of the major issues in software requirement prioritization is that the existing techniques handle only toy projects or software projects with very few requirements. The current techniques are not suitable for the prioritization of a large number of requirements in projects where requirements may grow to the hundreds or even thousands. The research paper proposes an expert system, called the Priority Handler (PHandler), for requirement prioritization. PHandler is based on the value-based intelligent requirement prioritization technique, neural network and analytical hierarchical process in order to make the requirement prioritization process scalable. The back-propagation neural network is used to predict the value of a requirement in order to reduce the extent of expert biases and make the PHandler efficient. Moreover, the analytical hierarchy process is applied on prioritized groups of requirements in order to enhance the scalability of the requirement prioritization process.

문제틀과 아키텍처 패턴의 매칭을 이용한 소프트웨어 아키텍처 설계 방법

While architectural patterns provide software development solutions by providing schemas for structural organizations of software systems based on empirical knowledge, Jackson's problem frames provide a method of analyzing software problems. Problem frames are useful to understanding the software development problem, by putting emphasis on the problem domain, rather than on the solution space. Research exists that relates problem frames and software architecture, but most of this research uses problem frames only to understand given problems. Moreover, none of the existing research derives architectural patterns by considering both problem frames and quality attributes. In this paper, we propose a software architecture design method for pattern-based architecture design, by matching problem frames and architectural patterns. To that end, our approach first develops the problem model based on the problem frames approach, and then uses it to match with candidate architectural patterns, from the perspectives of both functionality, and quality attributes. Functional matching uses the problem frame diagram to match the problem model of an architectural pattern. We conduct a case study to show that our approach can systematically decide the right architectural patterns, and provide a basis for fine-grained software architecture design.