Software Engineering Techniques Research Articles

Preparation of a Computer Software Program for the Feasibility Study of Livestock Enterprises

This study was carried out with the aim of developing a software program that will enable the breeder to decide easily during the preparation of the feasibility for livestock enterprises. For this purpose, 63 enterprises in Gaziantep and Sanliurfa provinces/Turkey were visited between the years 2021-2022 and all the data obtained were evaluated. The ‶C#″ programming language was used in the development of the software program. ‶Microsoft SQL Server″ database was used to store the obtained data. This feasibility program is a software program where productivity checks are performed for enterprises and their personnel. It is a program that can be used easily from a small-capacity enterprises to a large-capacity enterprises. The cost calculations are not included in the program due to the economic conditions of the market. As a result, this program, which was prepared by taking into account software engineering techniques, will provide great advantages and conveniences for enterprises. Risk factors will be determined and alternatives will be presented with this software program that performs enterprises efficiency testing. It has been concluded that this software will be a program that can be preferred by the breeder since it can be used on all computers and offers different alternatives in enterprises establishments.

Practitioners' expectations on automated release note generation techniques

AbstractThe software development life cycle relies heavily on the software release note, a crucial document. Various practitioners, including project managers and clients, benefit from release notes as they provide an overview of the latest software release. However, the manual generation of release notes is a time‐consuming and stressful task. Researchers have recently proposed automated techniques to generate release notes, saving developers' time and enhancing their understanding of software projects. Unfortunately, the adoption of these tools in practice remains limited. To address this gap, we have taken steps to understand the expectations and requirements of practitioners regarding release note generation techniques before implementing new automated approaches. Consequently, our approach involves two main stages: First, we conduct a comprehensive review of the relevant literature and analyze existing release notes from GitHub repositories to gain insights into the current practices. Second, we conduct an online survey study to gather input from practitioners and understand their expectations regarding release notes. We have reviewed 16 papers related to release notes and explored 3347 release notes from 21 GitHub repositories. Our analysis revealed key artifacts present in release note contents, including issues (29%), pull requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%). Additionally, we conducted a survey study involving 32 professionals to understand the essential information that should be included in release notes based on users' roles. For instance, project managers were more interested in learning about new features rather than less critical bug fixes. Furthermore, we identified gaps in existing systems and essential factors to consider when implementing release notes techniques in software engineering. The insights gained from our study can guide future research directions and assist practitioners in generating release notes with relevant content, thus improving the overall quality of documentation in software development.

A Maturity Level Framework for Practicing Machine Learning Operations in CI/CD For Software Deployment

Significantly shorter software development and deployment cycles have been made possible by the adoption of continuous software engineering techniques in business operations, such as DevOps (Development and Operations). Data scientists and operations teams have recently become more and more interested in a practice known as MLO (Machine Learning Operations). However, MLO adoption in practice is still in its early stages, and there aren't many established best practices for integrating it into current software development methods. In order to give a frame that outlines the way necessary in espousing MLO and the stages through which business capes process as they come riper and sophisticated, we achieve a methodical literature study as well as a slate review of literature in this composition. We test this approach in three example businesses and demonstrate how they were able to embrace and incorporate MLO into their massive software development businesses. This study offers three contributions. To give an overview of the state of the art in MLO, we first examine recent publications. Based on this analysis, we create an MLO framework that outlines the steps taken in the ongoing creation of machine learning models. Second, we define the several stages that businesses go through as they develop their MLO practices in a maturity model. Third, we map the firms to the maturity model phases and test our methodology using three embedded systems case companies. The main objective is to create an MLO framework.

Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles

As the process of urbanization continues to accelerate, the demand for sustainable cities has become more critical than ever before. The incorporation of electric vehicles (EVs) is a key component in creating sustainable cities. However, the development of smart cities for EVs entails more than just the installation of charging stations. Software engineering plays a crucial role in realizing smart cities for electric vehicles. This paper examines the role of software engineering in the creation of smart cities for electric vehicles, the techniques utilized in electric vehicle charging infrastructure, the obstacles faced by software engineers, and the future of software engineering in sustainable cities. Specifically, the paper explores the significance of software engineering in integrating EVs into the transportation system, including the design of smart charging and energy management systems, and the establishment of intelligent transportation systems. Additionally, the paper offers case studies to demonstrate successful software engineering implementations for smart cities. Finally, the paper concludes with a discussion of the challenges that software engineers encounter in implementing intelligent transportation systems for EVs and provides future directions for software engineering in sustainable cities.

QuoTe : Quality-oriented Testing for Deep Learning Systems

Recently, there has been significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is DL testing—that is, given a property of test, defects of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the neuron coverage metrics, which are commonly used by most existing DL testing approaches, are not necessarily correlated with model quality (e.g., robustness, the most studied model property), and are also not an effective measurement on the confidence of the model quality after testing. In this work, we address this gap by proposing a novel testing framework called QuoTe (i.e., Qu ality- o riented Te sting). A key part of QuoTe is a quantitative measurement on (1) the value of each test case in enhancing the model property of interest (often via retraining) and (2) the convergence quality of the model property improvement. QuoTe utilizes the proposed metric to automatically select or generate valuable test cases for improving model quality. The proposed metric is also a lightweight yet strong indicator of how well the improvement converged. Extensive experiments on both image and tabular datasets with a variety of model architectures confirm the effectiveness and efficiency of QuoTe in improving DL model quality—that is, robustness and fairness. As a generic quality-oriented testing framework, future adaptations can be made to other domains (e.g., text) as well as other model properties.

ReLo: a Dynamic Logic to Reason About Reo Circuits

Critical systems require high reliability and are present in many domains. They are systems in which failure may result in financial damage or even loss of lives. Standard techniques of software engineering are not enough to ensure the absence of unacceptable failures and/or that critical requirements are fulfilled. Reo is a component-based modelling language that aims to provide a framework to build software based on existing pieces of software, which has been used in a wide variety of domains. Its formal semantics provides grounds to certify that systems based on Reo models satisfy specific requirements (i.e., absence of deadlocks). Current logical approaches for reasoning over Reo require the conversion of formal semantics into a logical framework. ReLo is a dynamic logic that naturally subsumes Reo's semantics. It provides a means to reason over Reo circuits. This work extends ReLo by introducing the iteration operator, and soundness and completeness proofs for its axiomatization.

Decision Support System for Performance Assessment in Raising Teacher Salaries Using the SAW Method at MA'ARIF Vocational School

The Decision Support System is a specialized information system designed to aid management in making effective and efficient decisions on semi-structured problems; it does not replace conventional decision-making procedures. The system that is currently running at the Cicalengka Integrated Ma'arif Vocational School has deficiencies: The process of determining teacher salary increases at the Cicalengka Integrated Ma'arif Vocational School takes more time due to the difficulty of finding data; It's easy to make mistakes because the salary increase process is done manually; It's easy to lose the raise process file. The decision-making function increase in teacher salaries at SMK Ma'arif Terpadu Cicalengka. This thesis uses a Decision Support System with the SAW (Simple Additive Weighting) method then the system development method uses OOAD (object-oriented analysis and design) with the RUP (Rational Unified Process) model. Rational Unified Process (RUP) is a software engineering technique created by compiling the software development industry's best practices. RUP is object-oriented, with activities centered on model development utilizing the Unified Modeling Language (UML). The final result of the Thesis and the making of applications and Decision Support Systems Assessing Performance for Salary Increases/Teachers at SMK Ma'arif Terpadu Cicalengka is a website-based application that can facilitate the process of evaluating performance for salary increases at SMK Ma' Arif Integrated Cicalengka, The Decision Support System built using the SAW method is expected to make it easier for schools to evaluate performance for teacher/manager salary increases; The Decision Support System that is built can minimize errors that occur from the performance appraisal process for teacher/manager salary increases; Can reduce the loss of archives at Integrated Cicalengka Ma'arif Vocational High School.

Applying Human Values Theory to Software Engineering Practice: Lessons and Implications

The study of human values in software engineering (SE) is increasingly recognised as a fundamental human-centric issue of SE decision making. However, values studies in SE still face a number of issues, including the difficulty of eliciting values in a systematic and structured way, the challenges of measuring and tracking values over time, and the lack of practice-based understanding of values among software practitioners. This paper aims to help address these issues by: 1) outlining a research framework that supports a systematic approach to values elicitation, analysis, and understanding; 2) introducing tools and techniques that help elicit and measure values during SE decision making processes in a systematic way; and 3) applying such tools to a month-long research sprint co-designed with an industry partner and conducted with 27 software practitioners. The case study builds on lessons from an earlier pilot (12 participants) and combines in-situ observations with the use of two values-informed tools: the Values Q-Sort (V-QS), and the Values-Retro. The V-QS adapts instruments from values research to the SE context, the Values-Retro adapts existing SE techniques to values theory. We distil implications for research and practice in ten lessons learned.

Classification for the Concrete Syntax of Graph-Like Modeling Languages

A classification scheme for Graph-Like Modeling Languages (GLML) is presented in this paper. The novelty of this classifier lies in its application to a meta-model for GLML that deviates from the simple graph model and underlies a large number of GLML. The main goal of using this classification scheme is to support the reuse of layout algorithms for GLML. GLML are used directly or indirectly for the development of software by model-based software engineering techniques. In other domains, graph-like models are artifacts (e.g., circuit diagrams, energy flow diagrams) that serve as input for downstream specialized applications (simulators, optimizers). The concrete syntax of a language for creating, editing, and understanding models is highly important for the development of modeling tools. Layout methods for the used languages have to be implemented to achieve software tools with good usability. Developing layout algorithms is a complex topic that is covered by the specialized field of Graph Drawing. However, there is no existing procedure to determine which layout algorithm can be used for a GLML. Matching layout algorithms to GLML can be achieved by applying the presented classification scheme.

Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is devoid of bugs. One of the most time-consuming and costly software procedures is finding and fixing bugs. Although it is impossible to eradicate all bugs, it is feasible to reduce the number of bugs and their negative effects. To broaden the scope of bug prediction techniques and increase software quality, numerous causes of software problems must be identified, and successful bug prediction models must be implemented. This study employs a hybrid of Faster Convolution Neural Network and the Moth Flame Optimization (MFO) algorithm to forecast the number of bugs in software based on the program data itself, such as the line quantity in codes, methods characteristics, and other essential software aspects. Here, the MFO method is used to train the neural network to identify optimal weights. The proposed MFO-FCNN technique is compared with existing methods such as AdaBoost (AB), Random Forest (RF), K-Nearest Neighbour (KNN), K-Means Clustering (KMC), Support Vector Machine (SVM) and Bagging Classifier (BC) are examples of machine learning (ML) techniques. The assessment method revealed that machine learning techniques may be employed successfully and through a high level of accuracy. The obtained data revealed that the proposed strategy outperforms the traditional approach.

Quantum Software Components and Platforms: Overview and Quality Assessment

Quantum computing is the latest revolution in computing and will probably come to be seen as an advance as important as the steam engine or the information society. In the last few decades, our understanding of quantum computers has expanded and multiple efforts have been made to create languages, libraries, tools, and environments to facilitate their programming. Nonetheless, quantum computers are complex systems at the bottom of a stack of layers that programmers need to understand. Hence, efforts towards creating quantum programming languages and computing environments that can abstract low-level technology details have become crucial steps to achieve a useful quantum computing technology. However, most of these environments still lack many of the features that would be desirable, such as those outlined in The Talavera Manifesto for Quantum Software Engineering and Programming. For advancing quantum computing, we will need to develop quantum software engineering techniques and tools to ensure the feasibility of this new type of quantum software. To contribute to this goal, this paper provides a review of the main quantum software components and platforms. We also propose a set of quality requirements for the development of quantum software platforms and the conduct of their quality assessment.

Comparative Study of Software Testing Technique using Manually and Automated Way

Software could be diagnosed either manually or automatically. The two approaches are complementary: automated testing can perform a huge number of tests in short time or period, whereas manual testing uses the knowledge of the testing engineer to target testing to the parts of the system that are assumed to be more error-prone. Despite this contemporary, tools for manual and automatic testing are usually different, leading to decreased productivity and reliability of the testing process. AutoTest is a testing tool that provides a “best of both worlds” strategy: it integrates developers’ test cases into an automated process of systematic contract-driven testing. This allows it to combine the benefits of both approaches while keeping a simple interface, and to treat the two types of tests in a unified fashion: evaluation of results is the same, coverage measures are added up, and both types of tests can be saved in the same format. The objective of this paper is to discuss the Importance of Automation tool with associate to software testing techniques in software engineering. In this paper we provide introduction of software testing and describe the CASE tools. The solution of this problem leads to the new approach of software development known as software testing in the IT world. Software Test Automation is the process of automating the steps of manual test cases using an automation tool or utility to shorten the testing life cycle with respect to time.

Development of an app to monitor queues at an ophthalmological institute

Applications in the health area, known as these m-health, are important accessories for the management of productive systems, especially with the increase in demand for health services. Therefore, this study aims to develop an application for monitoring queues in an ophthalmology institute located in the São Francisco Valley region. The work performed as prototyping steps with Figma, analysis of the application development with Flutter/Dart and Firebase and evaluation of the management of the alpha version. The evaluation was based on the MARS scale, which obtained the dimension “functionality” with the highest evaluation (3.75) and “aesthetics” as the lowest (3.0). The overall quality was indicated as (3.70), above recommended (3.70). It is concluded that it was possible to develop an application using knowledge and techniques of Software Engineering and Queue Management.

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale.

The development of accurate transferable force fields is key to realizing the full potential of atomistic modeling in the study of biological processes such as protein-ligand binding for drug discovery. State-of-the-art transferable force fields, such as those produced by the Open Force Field Initiative, use modern software engineering and automation techniques to yield accuracy improvements. However, force field torsion parameters, which must account for many stereoelectronic and steric effects, are considered to be less transferable than other force field parameters and are therefore often targets for bespoke parametrization. Here, we present the Open Force Field QCSubmit and BespokeFit software packages that, when combined, facilitate the fitting of torsion parameters to quantum mechanical reference data at scale. We demonstrate the use of QCSubmit for simplifying the process of creating and archiving large numbers of quantum chemical calculations, by generating a dataset of 671 torsion scans for druglike fragments. We use BespokeFit to derive individual torsion parameters for each of these molecules, thereby reducing the root-mean-square error in the potential energy surface from 1.1 kcal/mol, using the original transferable force field, to 0.4 kcal/mol using the bespoke version. Furthermore, we employ the bespoke force fields to compute the relative binding free energies of a congeneric series of inhibitors of the TYK2 protein, and demonstrate further improvements in accuracy, compared to the base force field (MUE reduced from 0.560.390.77 to 0.420.280.59 kcal/mol and R2 correlation improved from 0.720.350.87 to 0.930.840.97).

Can Software Engineering Harness the Benefits of Advanced AI?

Artificial intelligence (AI) has allowed us to build systems beyond anything deemed possible earlier. Can we evolve existing techniques in software engineering to meet the needs of AI enabled systems or do we need to build unique and novel tools to do so?

The Pandora's box of social, process, and people debts in software engineering

AbstractIn software engineering, technical debt (TD) has been widely investigated, but debt regarding social issues, people, and processes has not been explored as much. It should be noted here that we use nontechnical debt (NTD) as an umbrella term to cover social, process, and people debts. Although the number of studies on NTD in software is increasing, the majority of them are descriptive rather than rigorous, and there is no systematic development of cumulative knowledge. As a result, identifying the fundamental causes of NTD and the associated mitigation techniques in software engineering is challenging. Therefore, this study investigates the scientific evidence regarding NTD till date by conducting a systematic mapping review of software engineering research between January 2000 and October 2021. The search strategy resulted in 175 studies, 17 of which were identified as unique and relevant primary papers. The primary studies show that NTD and TD are inextricably linked. In addition, this study also captured a plethora of causes and mitigation strategies for managing NTD and thus makes four important contributions: (i) highlighting state‐of‐the‐art NTD research; (ii) identification of the reported causes and mitigation strategies in the primary papers; and (iii) determination of opportunities for future NTD research.

Creating Formal Models from Informal Design Artefacts

The use of robust software engineering processes is essential in the design and development of interactive systems. This ensures that software is both functionally correct and also usable in the required context. There are a variety of software engineering techniques that can be used to consider different aspects of a system under construction, and at different stages in the development process. There is, however, a natural tension between formal approaches (typically used in the domain of safety-critical systems to consider functional correctness) and informal design approaches, which focus on users and user requirements in a manner which is accessible by stakeholders. In this paper we present two new approaches which enable a tighter coupling of informal design requirements with formal models. We present examples of these two approaches and discuss the benefits that transformations between the informal and formal provide. We also discuss the current limitations of such work along with recommendations about how these might be addressed.

Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue

The evolution of ontological engineering leaded authors to use some techniques of software engineering to design ontologies. Are obtained from these techniques the monolithic or modularized Ontologies. When is difficult to reuse some concepts of monolithic ontologies, modularized Ontologies facilitate ontology management, understandability and reuse. This paper aims to survey on ontology modularization techniques and their contribution in biomedical ontologies design. Modularization reposed on appropriated techniques and some challenges related to ontology reused, scalable querying, collaborative authoring, and distributed reasoning. For most of disease ontologies, more especially ontologies which reused IDO, these challenges are not considered, and most of them are implemented with OWL language and the novel mode to construct ontology’s purpose is to facilitate reuse and interoperability of ontologies ensured by modularization.

Prediction of Software Bugs Using FCM Fused Artificial Neural Network Classifier

The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is bug-free. Bug detection and prevention aims to provide high-quality software that reduces the cost and time required to fix a defect, increases productivity, and allows for complete customer satisfaction. While it will be impossible to eradicate all flaws, it will be able to reduce the number of problems and their negative influence on software quality. This may be accomplished by putting in place a bug prediction mechanism that focuses on increasing software quality by lowering defect density. To extract usable information, soft computing approaches such as data mining and machine learning algorithms can be used. Soft computing approaches have been used to solve the problem of software bug prediction in a number of researches. The ability to predict necessary bugs considerably sooner in the development process is a major incentive for this study. The purpose is to stimulate people's interest in software quality testing and encourage them to employ fuzzy c means clustering (FCM), artificial neural network (ANN), and machine learning approaches to solve challenges in the field of software project success prediction. The FCM fused ANN used for bug prediction model in this study to predict software reliability. With an accuracy of 98.237percent, this research effort exhibits the usefulness and applicability of FCM fused ANN technology.

The effects of continuous integration on software development: a systematic literature review

ContextContinuous integration (CI) is a software engineering technique that proclaims frequent activities to assure the software product health. Researchers and practitioners mention several benefits related to CI. However, no systematic study surveys state of the art regarding such benefits or cons.ObjectiveThis study aims to identify and interpret empirical evidence regarding how CI impacts software development.MethodThrough a Systematic Literature Review, we search for studies in six digital libraries. Starting from 479 studies, we select 101 empirical studies that evaluate CI in the context of software development. We thoroughly read and extract information regarding (i) the CI environment, (ii) findings related to the effects of CI, and (iii) the employed research methods. We apply a thematic synthesis to group and summarize the findings.ResultsExisting research has explored the positive effects of CI, such as better cooperation, or negative effects, such as adding technical and process challenges. From our thematic synthesis, we identify six themes: development activities, software process, quality assurance, integration patterns, issues & defects, and build patterns.ConclusionsEmpirical research in CI has been increasing over recent years. We found that much of the existing research reveals that CI brings positive effects to software development. However, CI may also bring technical challenges to software development teams. Despite the overall positive outlook regarding CI, we still find room for improvements in the existing empirical research that evaluates the effects of CI.