Software Development Time Research Articles

Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review

Bots are software systems designed to support users by automating specific processes, tasks, or activities. When these systems implement a conversational component to interact with users, they are also known as conversational agents or chatbots . Bots—particularly in their conversation-oriented version and AI-powered—have seen increased adoption over time for software development and engineering purposes. Despite their exciting potential, which has been further enhanced by the advent of Generative AI and Large Language Models, bots still face challenges in terms of development and integration into the development cycle, as practitioners report that bots can add difficulties rather than provide improvements. In this work, we aim to provide a taxonomy for characterizing bots, as well as a series of challenges for their adoption in software engineering, accompanied by potential mitigation strategies. To achieve our objectives, we conducted a multivocal literature review , examining both research and practitioner literature. Through such an approach, we hope to contribute to both researchers and practitioners by providing (i) a series of future research directions to pursue, (ii) a list of strategies to adopt for improving the use of bots for software engineering purposes, and (iii) fostering technology and knowledge transfer from the research field to practice—one of the primary goals of multivocal literature reviews.

Software component selection methods and techniques: a systematic review

<p>Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.</p>

Software Maintainability and Refactorings Prediction Based on Technical Debt Issues

Software maintainability is a crucial factor impacting cost, time and resource allocation for software development. Code refactorings greatly enhance code quality, readability, understandability and extensibility. Hence, accurate prediction methods for both maintainability and refactorings are vital for long-term project sustainability and success, offering substantial benefits to the software community as a whole. This article focuses on prediction of software maintainability and the number of needed code refactorings using technical debt data. Two approaches were explored, one compressing technical debt issues per software component and employing machine learning algorithms such as ExtraTrees, Random Forest, Decision Trees, which all obtained a high accuracy and performance. The second approach retained multiple debt issue entries and utilized a Recurrent Neural Network, although less effectively. In addition to the prediction of the requisite number of code refactorings and software maintainability for individual software components, a comprehensive analysis of technical debt issues was conducted before and after the refactoring process. The outcomes of this study contribute to the advancement of a dependable prediction system for maintainability and refactorings, presenting potential advantages to the software community in effectively managing maintenance resources. Of all the employed models, the ExtraTrees model yielded the most optimal predictive outcomes. To the best of our knowledge no other approaches of using ML techniques for this problem have been reported in the literarture. Keywords and phrases: Software Quality, Sonarqube, Refactoring, Code Smells.

Service recommendation based on contrastive learning and multi-task learning

Service recommendation is an efficient method for service-oriented software that can improve software quality. Applications often require the integration of multiple services to create more powerful and complex functionality while saving software development time. However, the vast number of available candidate Web services can impose a heavy burden on software developers’ selection decisions. The existing service recommendation challenges are mainly come from: (1) the development requirements entered by users are too arbitrary (2) the extreme sparsity of invocation records. To address the above challenges, in this paper, we propose a Service Recommendation method based on Contrastive Learning and Multi-task Learning (SRCLML). Specifically, we utilize the Transformer model to extract the development requirements of users, conduct in-depth mining of text descriptions, and extract features of applications. Next, the features are fed into the DNN model to predict the probability that the service will be selected. Moreover, we add a tag judgment task to make it capable of multi-task learning, through which, the training signal information implied can be used as an inductive bias to improve service recommendation capabilities. Additionally, we build three subgraphs based on the global graph, conduct in-depth mining of historical invocation records based on contrastive learning and graph neural network to extract features of applications and services and calculate application preferences for each service. Finally, we combined the above two to obtain the final recommendation service list. Extensive experiments on real-world datasets demonstrate that our method, SRCLML, outperforms several state-of-the-art comparison methods in the domain of service recommendation.

Towards Automation of the FORM/BCS Method

The software industry is facing more complex computer systems, with short development and sustainability issues. To deliver good software with these constraints, software reuse has become a central concept for minimizing design and realization costs. This study improves upon Feature-Oriented Reuse Method with Business Component Semantics (FORM/BCS), a software development method that produces adaptable architectures from reusable domain components. This is a promising method for reusable software assets and model creation. The objective of the FORM/BCS is to bring the industrial production chain to the software. This study proposes a model to automatically transform the FORM/BCS business subsystem component into a process business component. Two metamodels for business subsystems and process business components were developed. In addition, this study establishes correspondences between the source metamodel and target metamodel classes, transformation rules, and the instance of the source metamodel and generates the target metamodel instance. Detailed findings can help practitioners reduce software design costs and development time, and contribute to the advancement of knowledge in software engineering.

Agent-Module Approach to the Design and Implementation of Software Systems Based on Simulation Modeling

The paper examined the problem of modeling and design of software computer systems. The article briefly describes the content aspects of the various stages of the implementation of software complexes and systems. The methodology of research and analysis of systems, which uses the method of computer simulation modeling, is given. The aim of the work is to create a new universal approach to the design and implementation of software systems of various levels of complexity. During the study, the following general scientific methods were used: system analysis, synthesis, formalization, modeling, decomposition, structuring, description, comparison. The materials used in the study were: computers and software development tools. As a result of the study proposed a universal agent-module approach to the design and implementation of software systems based on simulation agent-based modeling. The study presents the processes of modeling and designing software systems, their stages, and the possibility of their integration within the described approach. Examples of formal model and structure of the software system under design based on the agent-module approach are presented. The proposed approach can be used in the process of software development at the initial stages related to modeling and designing of software systems. Integration of modeling and design stages in the proposed approach will significantly reduce software development time and simplify the design process by making it clearer and more formalized. Materials of the article are of scientific and practical value for a wide range of specialists interested in system analysis, modeling, design and implementation of software systems.

Detection of microservice‐based software anomalies based on OpenTracing in cloud

SummaryToday, the noticeable tendency of the software industry to break large software projects into loosely coupled modules through a microservice‐based architecture is more than ever. This is because of advantages such as scalability, independence, smaller and faster deployments, improved fault isolation, and flexibility. On the other hand, it should be noted that with the growth of microservice architecture, new complexities have emerged. We need to have a mature DevOps team to handle the complexity involved in maintaining and supporting systems, namely functional and non‐functional monitoring (anomaly monitoring and detection). This challenge can lead to a lot of software development time being spent monitoring and identifying anomalies. Existing approaches are not accurate enough to identify anomalies, and if they are able to identify them, they are unable to identify the category of the anomaly. Our approach in this research is to use distributed tracing with the help of machine learning algorithms to identify performance anomalies, the exact location of each anomaly, and predict its category. In this research, we implemented a software based on microservice architecture and then created a variety of anomalies over time (e.g., physical resources, virtual resources, database, application) to be able to evaluate the proposed model. The resulting dataset is publicly available. Our simulation results show that the proposed model is able to accurately identify the anomalies with 98% accuracy and their category with 99% accuracy.

Requirements management in DevOps environments: a multivocal mapping study

Attention is currently being focused on DevOps, which aims to reduce software development time by means of close collaboration between the development and operations areas. However, little effort has been made to determine the role of requirements management in DevOps. The objective of this study is to help both researchers and practitioners by providing an overview of the best practices regarding requirements engineering in DevOps and identifying which areas still need to be investigated. A multivocal mapping study has, therefore, been carried out in order to study which methodologies, techniques and tools are used to support requirements management in DevOps environments. After applying the review protocol, 37 papers from formal literature and 14 references from grey literature were selected for analysis. The general conclusions obtained after analysing these papers were that, within DevOps, more attention should be paid to: (1) the reuse of requirements in order to identify systems and software artefacts that can serve as a basis for the specification of new projects; (2) the communication of requirements between the different areas of an organisation and the stakeholders of a project; (3) the traceability of requirements in order to identify the relationship with other requirements, artefacts, tasks and processes; (4) non-functional requirements in order to identify the requirements of the operations area in the early phases of a project; and finally (5) specific requirements tools that should be seamlessly integrated into the DevOps toolchain. All these issues must be considered without ignoring the agile and continuous practices of development, operations and business teams. More effort must also be made to validate new methodologies in industry so as to assess and determine their strengths and weaknesses.

FEATURES OF THE ANGULAR SPEED DYNAMIC MEASUREMENTS WITH THE USE OF AN ENCODER

Based on the most significant features of the angular velocity dynamic measurements selected by the authors, the main phases of measuring information transformation were established, which allowed to obtain new mathematical models in the form of transformation function, equations for estimating quantization errors, analytical dependences for measuring range that are initial for modeling physical processes occurring in such digital measuring channels with microprocessor control. The process of converting an analog quantity into a binary code is analytically described, an equation for estimating the absolute and relative quantization error is obtained and a measurement range is established, which provides a normalized value of relative quantization error for angular velocity measuring channels with encoder. For the first time, the equation of sampling error was obtained, and it was proved that the limiting factor of the angular velocity measurements upper limit is not only the normalized value of quantization error, as previously thought, but also the value of sampling frequency fD. Therefore, to expand the measurement range (by increasing the upper limit of measurement), it is proposed not only to increase the speed of analog-to-digital conversion hardware, but also to reduce the execution time of software drivers for transmitting measurement information to RAM of microprocessor system. For this purpose, the analytical dependences of estimating the upper limit of measurement based on the value of the sampling step for different modes of measurement information transmission are obtained. The practical implementation of the software mode measurement information transmission is characterized by a minimum of hardware costs and maximum execution time of the software driver, which explains its low speed, and therefore provides a minimum value of the upper limit measurement. In the interrupt mode, the upper limit value of the angular velocity measurement is higher than in the program mode due to the reduction of the software driver’s execution time (tFl = 0). The maximum value of the angular velocity measurements upper limit can be achieved using the measurement information transmission in the mode of direct access to memory (DMA) by providing maximum speed in this mode (tFl = 0, tDR = 0). In addition, the application of the results obtained in the work allows at the design stage (during physical and mathematical modeling) to assess the basic metrological characteristics of the measuring channel, aimed at reducing the development time and debugging of hardware, software, and standardization of their metrological characteristics.

Mapping Processing Elements of Custom Virtual CGRAs onto Reconfigurable Partitions

FPGAs can provide application-specific acceleration for computationally demanding tasks. However, they are rarely considered general-purpose platforms due to low productivity of software development and long reconfiguration time. These problems can be mitigated by implementing a coarser overlay atop the FPGA fabric. Combining this approach with partial reconfiguration allows for the modification of individual processing elements (PEs) of the virtual architecture without altering the rest of the system. Module relocation can be used to share implementation details between functionally equivalent PEs that use identical sets of resources, thus eliminating redundant placement and routing runs. Proper floorplanning is crucial for virtual Coarse-Grained Reconfigurable Architectures (CGRAs) with relocatable PEs considering their tendency to use nearest-neighbor connection patterns. It requires solving two problems—finding identical regions in the FPGA fabric and assigning individual partitions to certain locations. This article presents minor improvements of a state-of-the-art solution for the first and proposes a novel technique for solving the other. The proposed automated floorplanner uses modified breadth-first search with direction-based penalties to create initial floorplan consistent with geometry of logical array, then improves the result with 2-opt local optimization. Compared to simulated annealing solutions, the proposed approach allows for the reduction in the floorplanning time by two to three orders of magnitude without compromising the quality of the results.

Model-Driven in Serios Games and Serious Games with User-Centered Design in the Last Decade: A Review

This paper reviews the literature on automatic code generation of user-centered serious games. We decided to break the study in two parts: one study about serious games with model driven engineering, and another study about user-centered serious games. This paper presents an extension of a paper presented at CONISOFT 20 where a systematic review of 5 years old at the time of writing was presented exclusively. The systematic literature review conducted in this paper covers a decade of information from January 2012 to June 2022. The main objective is to know the literature that helps to mitigate the costs and time of software development in serious games. The overall conclusion is that there is still work to be done to combine serious user-centered games and automatic generation. This paper is a systematic review that identifies relevant publications and provides an overview of research areas and publication venues. In addition, Research perspectives were classified according to common objectives, techniques, and approaches. Finally, is presented point out challenges and opportunities for future research and development.

ВЕРИФИКАЦИЯ ОШИБОК ДЕНОТАТИВНОЙ И СИГНИФИКАТИВНОЙ СЕМАНТИК В ДИАГРАММНЫХ СПЕЦИФИКАЦИЯХ ПОТОКОВ РАБОТ В АВТОМАТИЗИРОВАННОМ ПРОЕКТИРОВАНИИ

The author describes semantic errors of denotative and significative classes in the diagrammatic specifications of workflows (DWS), and reveals signs of synonymy and antonymy of temporal graphic words, conversivity and nesting of routes in the DWS. The article develops new methods of verification (an analysis with proved properties required) of DWS for errors in synonyms and antonyms of graphic words, conversivity and nesting of routes in the automated design of hardware and software of radio electronics. New methods contribute to the replacement of the designer’s manual labor during the verification of denotative, significative errors in the DWS with a computerized one, thereby reducing the development time of radio electronics hardware and software in computer-aided design and increasing the correctness of the DWS. The results of comparative studies of the DWS verification in the automated design of hardware and software manually and based on the proposed automated methods showed a reduction in the time spent on the DWS verification in the range from 17 minutes to 2 hours and 8 minutes, and also revealed errors in the DWS that were not found during the examination of the DWS by specialists. The proposed method is used at a large radio-electronic design and industrial enterprise JSC ‘Ulyanovsk Mechanical Plant’, Ulyanovsk in the development of hardware and software for the products of the enterprise. The material of this work is related to the author’s publication in the previous issue of the journal «Automation of Management Processes» No. 4 (65) of 2021.

Implementing COCOMO II as personnel direct cost in an owner estimate cost model for software project

COCOMO II is a method to get effort, time (schedule), and cost for software development. In this study, COCOMO II was used to obtain personnel direct costs of a software development project. Personnel direct costs were one of the three components that compose the owner estimate cost. The purpose of this study was to propose a conceptual model to determine the owner estimate cost that was formed from three components, namely: personnel direct cost, non-personnel direct costs, and taxes. In this study, it was explained the stages of using the model to determine the owner estimate cost for software development projects.

PRINCIPLES FOR DEVELOPMENT OF PROGRAM INTERFACES IN INDUSTRIAL INFORMATION, MEASURING, AND CONTROLLING SYSTEMS

Industrial information, measuring, and controlling systems have a program module designed to convert measurement results into data for display and control signals. A program module interconnected to other modules by program interfaces. In this case, data transformations are necessary when they are sent between modules. Object-oriented design patterns can be used when programming data transformations. When converting measurement results to objects, the Adapter design pattern can be applied. Programming the Command pattern is intended for converting objects into control signals. Data processing should be separated from their representation, storage and transmission. Functions between modules can be divided using the Model–View–Controller pattern. The use of design patterns reduces the development time and subsequent modification of software for both information, measuring, and control systems, as well as systems in other subjects of science and economics.

Online Presence System Development Website-Based using Prototype Methods

In the current era of the Covid-19 pandemic, almost all activities use technology, such as the lecture process. Almost all of the lecturing processes in high education are carried out online because it is to reduce the risk of contracting covid-19. The manual presences system by filling out forms or signatures is no longer effective because it has to be done offline. The online presence system is a system that is needed to find out about lectures, the discipline of teaching and students. The method used in the development of this system is Prototype. This method has the advantage of paying close attention to user needs and a faster time for software development. The results of this study are a system for online presence in the era of the Covid-19 pandemic in real-time with photo evidence. Teaching can validate and change the presence of students. In addition, the admin can easily recap the number of teaching and students presences for reporting. Teaching also finds it helpful to take the results of student presences for reporting the completeness of the assessment data and the head of the study program can see the presences summary report online. This system has been tested for functionality with an average result of 83.5% so that it can be said to be successfully built according to user needs

Research on an Adaptive Heterogeneous Database Middleware and Visualization Technology

In order to improve the efficiency of MIS development based on heterogeneous data sources, an adaptive heterogeneous database middleware and key visualization technology are proposed to realize the automatic creation of data access layers through visualization operations on the basis of heterogeneous databases. This research focused on the general access mode of various data sources in middleware, the interface of data and operation mapping between entity classes and object-oriented virtual heterogeneous databases, the associated interface technology between associated entity classes and corresponding data tables, the interface technology of cross-table and cross-database operation, the algorithm model for optimizing data access, the visualized operation interface, with the purpose is to provide a universal, powerful and easy-to-use middleware for the development of WEB-based MIS software. It will enable program developers to complete the development of data access layers (library building, entity class creation, data access operation method establishment, etc.) through simple visualized operations, and generate program code for the data access layer, which will greatly reduce the development workload and time of the MIS software in this procedure and improve the efficiency of software development.

Code Generation for Productive, Portable, and Scalable Finite Element Simulation in Firedrake

Creating scalable, high-performance PDE-based simulations requires an appropriate combination of models, discretizations, and solvers. The required combination changes with the application and with the available hardware, yet software development time is a severely limited resource for most scientists and engineers. Here we demonstrate that generating simulation code from a high-level Python interface provides an effective mechanism for creating high-performance simulations from very few lines of user code. We demonstrate that moving from one supercomputer to another can require significant algorithmic changes to achieve scalable performance, but that the code generation approach enables these algorithmic changes to be achieved with minimal development effort.

A Review on Test Automation for Test Cases Generation using NLP Techniques

In the process of product development, stakeholders and top management summarize the concept and document the requirements in natural language. These ideas and descriptions documented as software requirements by the technical department. Developers develop software following this software requirement document. For testing this developed software, they derive test cases from natural language requirements and then do the testing process to find the bugs. This process involves understanding requirements and derives test cases that are used to understand by developers and testers. Due to increasing the advanced features, deriving the test cases is monotonous and takes more time. This research article shows a method to automate this process which is deriving test cases from requirements using NLP algorithms. This approach useful to reduce the time and cost of software development.

COMPARATIVE ANALYSIS OF SOFTWARE EFFORT ESTIMATION USING DATA MINING TECHNIQUE AND FEATURE SELECTION

Software development involves several interrelated factors that influence development efforts and productivity. Improving the estimation techniques available to project managers will facilitate more effective time and budget control in software development. Software Effort Estimation or software cost/effort estimation can help a software development company to overcome difficulties experienced in estimating software development efforts. This study aims to compare the Machine Learning method of Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree Random Forest (DTRF) to calculate estimated cost/effort software. Then these five approaches will be tested on a dataset of software development projects as many as 10 dataset projects. So that it can produce new knowledge about what machine learning and non-machine learning methods are the most accurate for estimating software business. As well as knowing between the selection between using Particle Swarm Optimization (PSO) for attributes selection and without PSO, which one can increase the accuracy for software business estimation. The data mining algorithm used to calculate the most optimal software effort estimate is the Linear Regression algorithm with an average RMSE value of 1603,024 for the 10 datasets tested. Then using the PSO feature selection can increase the accuracy or reduce the RMSE average value to 1552,999. The result indicates that, compared with the original regression linear model, the accuracy or error rate of software effort estimation has increased by 3.12% by applying PSO feature selection

Heroku-Based Innovative Platform for Web-Based Deployment in Product Development at Axis

The introduction of cloud technology has reduced the feedback time in software development for many companies. However, companies require significant resources to run applications in an Infrastructure as a Service. The study aims at developing an innovation platform which enables faster deployment of web-based applications. The innovation platform is an add-on for a cloud service, its purpose is to allow developers to set up and use multiple cameras in a cloud environment. Furthermore, the study investigates the drivers and value created by the platform for Axis. We developed a proof of concept built on the innovation platform, using the design science methodology and evaluated it in a focus group. The prototype was a mock version of a grocery store’s internal website, used to show the potential of the innovation platform. Moreover, we conducted semi-structured interviews to investigate the drivers for implementing the platform and the value it created. Results showed that the implementation of the prototype may make the deployment of web-based applications easier. A majority of the interviewees and the focus group participants agreed that the development of the innovation platform should continue and be tested to adopt the platform as a complement to AWS. However, the degree to which the platform should be open source needs a more clear management strategy. The prototype is seen as an innovation platform which may be used to quickly experiment with new ideas. The key drivers for implementing a prototype entails reduced cost, faster time to market and reduced complexity of web-based deployments.