Step In Software Development Research Articles

Extracting Requirements of E-learning Systems

Extracting functional & non-functional requirements is a basic step in software development. The process of requirements engineering includes seven main activities Elicitation, Negotiation, Specification, Modeling, Verification & Validation, Management, and Traceability, this study focuses on requirements engineering in E-learning systems to be performed in all educational systems. Non-functional requirements are considered more critical than functional requirements because the non-functional requirements can affect all the systems and some of non-functional requirements may become functional requirements like security. So, hoping that this constructed study is going to help the developers of the system to understand the requirements to build E-learning systems, especially in higher educational institutions.

Estimating software reliability using size-biased modelling

Software testing is an important step in software development where inputs are administered repeatedly to detect bugs present in the software. In this paper, we have considered the estimation of total number of bugs and software reliability as a size-biased sampling problem by introducing the concept of eventual bug size as a latent variable. We have developed a Bayesian generalised linear mixed model (GLMM) using software testing detection data to estimate software reliability and stopping phase. The model uses size-biased approach where the probability of detecting a bug is an increasing function of eventual size of the bug which is as an index for the potential number of inputs that may eventually pass through the bug. We have tested the sensitivity of the reliability estimates by varying the number of inputs and detection probability via a simulation study and have found that the key parameters could be accurately estimated. Further, we have applied our model to two empirical data sets – one from a commercial software and the other from ISRO launch mission software testing data set. The hierarchical modelling approach provides a unified modelling framework that may find applications in other fields (e.g. hydrocarbon explorations) apart from software management.

Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.

Software Defect Prediction Using Non-Dominated Sorting Genetic Algorithm and $k$-Nearest Neighbour Classifier

Background: Software Defect Prediction (SDP) is a vital step in software development. SDP aims to identify the most likely defect-prone modules before starting the testing phase, and it helps assign resources and reduces the cost of testing. Aim: Although many machine learning algorithms have been used to classify software modules based on static code metrics, the k-Nearest Neighbors (kNN) method does not greatly improve defect prediction because it requires careful set-up of multiple configuration parameters before it can be used. To address this issue, we used the Non-dominated Sorting Genetic Algorithm (NSGA-II) to optimize the parameters in the kNN classifier with favor to improve SDP accuracy. We used NSGA-II because the existing accuracy metrics often behave differently, making an opposite judgment in evaluating SDP models. This means that changing one parameter might improve one accuracy measure while it decreases the others. Method: The proposed NSGAII-kNN model was evaluated against the classical kNN model and state-of-the-art machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes (NB), and Random Forest (RF) classifiers. Results: Results indicate that the GA-optimized kNN model yields a higher Matthews Coefficient Correlation and higher balanced accuracy based on ten datasets. Conclusion: The paper concludes that integrating GA with kNN improved defect prediction when applied to large or small or large datasets.

Development of Mobile Application-Based Assisttive Technology to Improve Slow Learner Students' Conjecturing Ability

Development of software for mobile applications has grown in significance in the current digital era. The goal of this project is to create mobile-based software that can enhance slow learners' conjecturing skills during the learning process. Rapid Application Development (RAD), a technique well-known for its iterative and collaborative approach, was utilized to create this program. Software development that is quick, flexible, and more user-centered is made possible by RAD. The steps of software development, such as user requirement analysis, application design, implementation, and testing, were discussed in this study. We also emphasize the importance of actively involving users in the development process to ensure that the software meets their needs and expectations. The results of software quality testing show that the software developed meets ISO-9126 standards. It covers important aspects such as reliability, efficiency and usability, earning Good criteria. Meanwhile, only the maintainability and portability aspects received sufficient criticality. It can be concluded that in general the quality of the software developed is good in accordance with ISO-9126.

Automated Testing of Indo Towing Information System with Katalon Studio

The development of web-based and mobile information systems that support various aspects of human work has become the main focus in the current technological era. The importance of automation testing in ensuring software quality is becoming increasingly profound. The quality of these systems is critical to supporting successful business operations. Automation testing using the STLC method and equivalence partitioning technique is applied to ensure the quality of the Indo Towing system. The testing tool used is Katalon Studio, which facilitates the implementation of automated testing with efficiency. Thus, this study emphasizes the importance of automation testing as a crucial step in software development. The results of the implementation of automation testing using Katalon Studio were successfully carried out according to the information system function without any bugs being found in the system. Tests carried out automatically using Katalon Studio can be concluded that the system is running well.

Implementing code review in the scientific workflow: Insights from ecology and evolutionary biology.

Code review increases reliability and improves reproducibility of research. As such, code review is an inevitable step in software development and is common in fields such as computer science. However, despite its importance, code review is noticeably lacking in ecology and evolutionary biology. This is problematic as it facilitates the propagation of coding errors and a reduction in reproducibility and reliability of published results. To address this, we provide a detailed commentary on how to effectively review code, how to set up your project to enable this form of review and detail its possible implementation at several stages throughout the research process. This guide serves as a primer for code review, and adoption of the principles and advice here will go a long way in promoting more open, reliable, and transparent ecology and evolutionary biology.

Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

Software maintenance is an imperative step in software development. Code smells can arise as a result of poor design as well as frequent code changes due to changing needs. Early detection of code smells during software development can help with software maintenance. This work focuses on identifying code smells on Java software using nanopatterns. Nanopatterns are method-level code structures that reflect the presence of code smells. Nanopatterns are extracted using a command-line interface based on the ASM bytecode analysis. Class labels are extracted using three tools, namely inFusion, JDeodorant, and iPlasma. Rules are extracted from nanopatterns using the Apriori algorithm and mapped with the extracted class labels. Best rules are selected using the Border Collie Optimization (BCO) algorithm with the accuracy of the k-NN classifier as the fitness function. The selected rules are stored in the rule base to detect code smells. The objective is to detect a maximum number of code smells with a minimum number of rules. Experiments are carried out on Java software, namely jEdit, Nutch, Lucene, and Rhino. The proposed work detects code smells, namely data class, blob, spaghetti code, functional decomposition, and feature envy, with 98.78% accuracy for jEdit, 97.45% for Nutch, 95.58% for Lucene, and 96.34% for Rhino. The performance of the proposed work is competitive with other well-known methods of detecting code smells.

Extracting Functional and Non-Functional Requirements for E-learning Systems

Extracting functional & non-functional requirements is a basic step in software development. The process of requirements engineering includes seven main activities Elicitation, Negotiation, Specification, Modeling, Verification & Validation, Management, and Traceability, this study focuses on requirements engineering in E-learning systems to be performed in all educational systems. Non-functional requirements are considered more critical than functional requirements because the non-functional requirements can affect all the systems and some of non-functional requirements may become functional requirements like security. So, hoping that this constructed study is going to help the developers of the system to understand the requirements to build E-learning systems, especially in higher educational institutions.

Software Development for the Use of Generalized Parabolic Blending in Data Prediction Processes

Parabolic blending (PB) is one of the important topics in applied mathematics and computer graphics. The use of generalized parabolic blending (GPB) for different scenarios adds flexibility to the polynomial. Overhauser (OVR) elements is a special case in GPB (r=0.5, s=0.5). GPB can also be used in estimation. In this study, data obtained from thickness distribution of a 3mm thick high impact polystyrene product after thermoforming using a mold was used for data estimation. For this purpose, software has been developed. The software development steps and formula usages are explained. Using the developed software, polynomials for GPB and default PB (OVR) were created. The data set was compared with the y values produced by the polynomials for certain x values. At the end of the research, it was determined that the results obtained from the GPB were 0.1728 percent more accurate than the data obtained from the PB for the default values.

Api test automation of search functionality with artificial intelligence

One of the steps in software development is to test the software product. With the development of technology, the testing process has improved to automated testing, which reduces the impact of the human factor on error and speeds up testing. The main software products for testing are considered to be web applications, web services, mobile applications and performance testing. According to the testing pyramid, when testing web services, you need to develop more test cases than when testing a web application. Because automation involves writing software code for testing, the use of ready-made tools will speed up the software development process. One of the most important test indicators is the coverage of search functionality. The search functionality of a web application or web service requires a large number of cases, as you need to provide many conditions for its operation through the free entry of any information on the web page. There is an approach to data-based testing, which involves working with a test data set through files such as CSV, XLS, JSON, XML and others. However, finding input for testing takes a lot of time when creating test cases and automated test scenarios. It is proposed to use artificial data set generators based on real values and popular queries on the website to form a test data set. In addition, it is possible to take into account the probable techniques of developing test cases. It is proposed to conditionally divide the software for testing into several layers: client, test, work with data, checks and reports. The Java programming language has a number of libraries for working at each of these levels. It is proposed to use Rest Assured as a Restful client, TestNG as a library for writing tests with checks, and Allure report for generating reports. It is noted that the proposed approach uses artificial intelligence for automated selection of test cases when creating a test to diversify test approaches and simulate human input and behavior to maximize the use of cases.

Towards Generating Unit Test Codes Using Generative Adversarial Networks

Unit testing is one of the important software development steps to ensure the software’s quality. Despite its importance, unit testing is often neglected since it requires a significant amount of time and effort from the software developers to write them. Existing automated testing generating systems from past research still have shortcomings due to the Genetic Algorithm (GA) limitations to generate the appropriate unit test codes. This study explores the feasibility of using Generative Adversarial Networks (GAN) models to generate unit test code with the ability of GAN to cover GA’s drawbacks. We perform experimentations using four state-of-the-art GAN models to generate basic unit test codes and compare the results by analyzing the generated output codes using novel metrics proposed from past studies as well as performing qualitative evaluation on the generated outputs. The results show that the generated codes have satisfactory quality scores (BLEU-2 of around 99%) from the models and adequate diversity score (NLL-Div and NLL-Gen) in most models. Our study shows positive indications and potential in the use of GAN for automatic unit test code generation and suggests recommendations for future studies in GAN-based unit test code generation systems

INTEGRATION OF URBAN SPATIAL DATA MANAGEMENT AND VISUALIZATION WITH ENTERPRISE APPLICATIONS USING OPEN-SOURCE SOFTWARE

Abstract. In recent years, efficient management of urban spatial data has played a major role in improving urban planning projects both in terms of cost and time savings. Since urban planning projects involve various disciplines like city planning and architecture as well as working with different spatial data, one of the main challenges is how to integrate and manage these multimodal data for a proper workflow. Currently, the involved companies are using project management and accounting systems, so called Enterprise Resource Planning (ERP) systems to handle these complex urban projects - that partly handle the same data objects as stored in urban spatial databases but without any spatial reference. Embedded in the application example of an urban redevelopment area, which according to the German Urban Development Promotion Act aims at financially promoting urban districts in need of renewal, project-related spatial and non-spatial data that were previously kept separate are linked and integrated. Therefore, our work presented here bridges the gap between these two types of application systems, the non-spatial accounting system called Finanz Management System (FMS) and the urban spatial databases. FMS manages information related to parcels, buildings, property owners, as well as the legally required payments connected to urban development, while an urban spatial database manages the geodata. We describe the prerequisites, procedures, and software development steps for coupling different types of applications by providing an example of the Enterprise Application Integration System (EAI). Our innovative integration process aims at making information from the spatial database available in FMS and vice versa, and allows updating the corresponding databases. Our work shows the potential of open-source software for cadastral data processing and visualization as well as accounting procedures for urban planning projects.

Building a model for analysis of dependencies of application software modules

Dependency analysis of software modules in applications is an important step in software development, but it almost always remains in the shadow of more general and frequent problems. The creation of a general mathematical model describing dependency analysis at a fundamental level is one of the tasks that can accelerate the development of new utilities for dependency analysis for a wide variety of module systems and programming languages.

INFORMATION SYSTEMS FOR MACHINE INTELLIGENCE TO AUTOMATED SOFTWARE TESTING

The methods of development software develop rapidly. The testing of software has a great role in developing a good product. Many technologies assembled into all aspects of performance, based on software testing. Many advanced automation tools use in a set of test design and validation tests based on the artificial intelligence. The important thing is to focus on changes, to work on basis of collective reasoning of the test command and other commands analogues. The methods of the quality testing are based on the information provided in the modern digital world. The business is relying on new fast processes to provide automatic testing of software. Applying approaches of solutions to financial organization allows increase the transparency of all steps of software development. These steps can help systems show more percentage of the test case rate, can save time and money, but also effectively solves the problem of scaling the process and errors. In this paper, we research information systems for machine intelligence to automated software testing. The aim is divided to tasks: the importance of artificial intelligence, the necessary stage of Software Development - Testing and Quality Controlling System, research of main automation tools. We concluded that use of intellectual intelligence and machine learning: allows automating the repeating process and usage of the database; delivers superb intellectual product; adapts to the progressive algorithm of learning; adds more depth analysis of multiple objects; allows retrieving the maximum amount of data from the databases.

PROTOTYPING OF BUKHARA CULINARY APPLICATION

The purpose of this research is to build a prototype of Bukhara Culinary Application using informal prototyping method. The difference between this application and the existing ones is the availability of ingredients and history information. This application will also provide delivery service to the customers. Customers and drivers register to this application, so that their details will be recorded. This might not seem much different from the available applications, but this research is also conducted to portray the use of informal prototyping. It is used for the ease in accommodating changes and continuing to the next step of software development by using commonly used software such as Adobe Photoshop. This research resulted in a prototype that provide the described information for 17 dishes from 5 restaurants in Bukhara.

Distributed Cat Modeling Based Agile Framework for Software Development

Software development is a hectic process that requires keen understanding and effective model such that the developed software inherits good quality, reliability, and achieves customer satisfaction towards achieving the goals effectively. The effective operation of the software is enabled by modifying the operating modules of the software through a model, like agile. In this paper, the catastrophic and distributed computing models are integrated into the software development step. The proposed model is termed as Distributed Cat model that is developed with the aim to handle the risk factors engaged in various developing stages of the agile model. The risk factors that affect the communication, planning, release, design, coding and testing modules of the agile modules are deeply learned and executed such that the risk factors are tackled by various modules present in the proposed Distributed Cat model. The effectiveness of the proposed model is analyzed based on the performance metrics, such as Index of Integration (IoI) and Usability Goals Achievement Metric (UGAM), for which five products including the hotel management system, Customer relationship Management system (CRM), Rainfall prediction system, Temperature monitoring system, and meta-search system, are employed and analyzed. The analysis is progressed using the parameters, like mean difference, variance, standard devaiation, correlation. The analysis proves that the proposed model offered a great deviation after applying the proposed model contributing a high degree of performance in software development.

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks.

Software performance testing

The article provides an overview of existing performance testing types. A test plan using the Jmeter tool is considered, the ability to compile tests and analyze them to obtain results is provided, while ensuring appropriate quality and ease of use. Software testing is one of the most important steps in software development. The universal approach to test development is complicated by the sheer data volume, the availability of various techniques and tools. Of particular importance is testing the computational performance of applications. Compared to other types of testing, this type is extremely complex and requires some personnel skills. Performance is mostly tested by automated methods, but statistical testing becomes important in parametric program research.

Bug Triaging Based on Tossing Sequence Modeling

Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging: 1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose iTriage which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches.