Software Engineering Tools Research Articles

An architecture for model-based and intelligent automation in DevOps

The increasing complexity of modern systems poses numerous challenges at all stages of system development and operation. Continuous software and system engineering processes, e.g., DevOps, are increasingly adopted and spread across organizations. In parallel, many leading companies have begun to apply artificial intelligence (AI) principles and techniques, including Machine Learning (ML), to improve their products. However, there is no holistic approach that can support and enhance the growing challenges of DevOps. In this paper, we propose a software architecture that provides the foundations of a model-based framework for the development of AI-augmented solutions incorporating methods and tools for continuous software and system engineering and validation. The key characteristic of the proposed architecture is that it allows leveraging the advantages of both AI/ML and Model Driven Engineering (MDE) approaches and techniques in a DevOps context. This architecture has been designed, developed and applied in the context of the European large collaborative project named AIDOaRt. In this paper, we also report on the practical evaluation of this architecture. This evaluation is based on a significant set of technical solutions implemented and applied in the context of different real industrial case studies coming from the AIDOaRt project. Moreover, we analyze the collected results and discuss them according to both architectural and technical challenges we intend to tackle with the proposed architecture.

Revealing the Unseen: AI Chain on LLMs for Predicting Implicit Data Flows to Generate Data Flow Graphs in Dynamically-Typed Code

Data flow graphs (DFGs) capture definitions (defs) and uses across program blocks, which is a fundamental program representation for program analysis, testing and maintenance. However, dynamically-typed programming languages like Python present implicit data flow issues that make it challenging to determine def-use flow information at compile time. Static analysis methods like Soot and WALA are inadequate for handling these issues, and manually enumerating comprehensive heuristic rules is impractical. Large pre-trained language models (LLMs) offer a potential solution, as they have powerful language understanding and pattern matching abilities, allowing them to predict implicit data flow by analyzing code context and relationships between variables, functions, and statements in code. We propose leveraging LLMs’ in-context learning ability to learn implicit rules and patterns from code representation and contextual information to solve implicit data flow problems. To further enhance the accuracy of LLMs, we design a five-step Chain of Thought (CoT) and break it down into an AI chain, with each step corresponding to a separate AI unit to generate accurate DFGs for Python code. Our approach’s performance is thoroughly assessed, demonstrating the effectiveness of each AI unit in the AI Chain. Compared to static analysis, our method achieves 82% higher def coverage and 58% higher use coverage in DFG generation on implicit data flow. We also prove the indispensability of each unit in the AI Chain. Overall, our approach offers a promising direction for building software engineering tools by utilizing foundation models, eliminating significant engineering and maintenance effort, but focusing on identifying problems for AI to solve.

Adoption of automated software engineering tools and techniques in Thailand

Readiness for the adoption of Automated Software Engineering (ASE) tools and techniques can vary according to the size and maturity of software companies. ASE tools and techniques have been adopted by large or ultra-large software companies. However, little is known about the adoption of ASE tools and techniques in small and medium-sized software enterprises (SSMEs) in emerging countries, and the challenges faced by such companies. We study the adoption of ASE tools and techniques for software measurement, static code analysis, continuous integration, and software testing, and the respective challenges faced by software developers in Thailand, a developing country with a growing software economy which mainly consists of SSMEs (similar to other developing countries). Based on the answers from 103 Thai participants in an online survey, we found that Thai software developers are somewhat familiar with ASE tools and agree that adopting such tools would be beneficial. Most of the developers do not use software measurement or static code analysis tools due to a lack of knowledge or experience but agree that their use would be useful. Continuous integration tools have been used with some difficulties. Lastly, although automated testing tools are adopted despite several serious challenges, many developers are still testing the software manually. We call for improvements in ASE tools to be easier to use in order to lower the barrier to adoption in small and medium-sized software enterprises (SSMEs) in developing countries.

Applications of Data Mining in Software Engineering: Techniques, Trends, and Case Studies

Data mining, the process of discovering patterns and knowledge from large quantities of data, has come a vital tool in software engineering, transubstantiating colorful aspects of the field through its advanced logical capabilities. This paper explores the different operations of data mining ways in software engineering, pressing their impact on perfecting software quality, effectiveness, and operation. We give a comprehensive overview of crucial data mining styles, including bracket, clustering, association rule mining, anomaly discovery, and textbook mining, and their specific operations within software disfigurement vaticination, quality assurance, conservation, design operation, and security. Through an analysis of contemporary case studies and empirical data, this paper illustrates how data mining ways have been effectively employed to prognosticate disfigurement-prone areas in software, optimize testing processes, and enhance design estimation and monitoring. Also, we address the challenges and limitations encountered in enforcing data mining results and bandy arising trends similar as the integration of data mining with machine literacy and artificial intelligence.

Designing the user interface of a ventilator under the constraints of a pandemic

Abstract This paper reports on the design of the GUI component of the PV1000, a fully-functional, cost-efficient pandemic ventilator developed during the COVID-19 pandemic. Our main objective in designing the user interface was to expose the rich features of the ventilator, which are on-par with many commercial medical devices, through an interface that is simple to use even by medical staff with little prior experience with ventilators. We present an agile development process which is intended to address the fast changing and potentially unclear requirements of a pandemic. This is to address the specific situation where domain experts as well as specific hardware have shortages, resulting in recurring changes in requirements. This occurs in conjunction with the collaboration of developers, not all of whom are familiar with the development of medical devices. Despite the pandemic situation, we have made a deliberate decision to use existing software engineering tools such as continuous integration. Additionally, we are employing a multi-platform development framework in order to utilise the resulting advantages, for example, if there are changes in requirements. We present and discuss the system, hardware and software architecture of the components that allow users to interact with the device. The focus here is on the dynamic generation of parts of the software architecture, which enables rapid adaptation to changing conditions on the part of the software. The user interface is evaluated through a user survey, where the majority of participants describe the interface as clear and intuitive. The entire ventilator was evaluated in one animal trial and fortunately never had to be used on humans. The next step is to convert the setup into an open source project that can be used as a research platform for ventilators.

Structural Analysis of an Off-Road Vehicle Suspension System Using CATIA and ANSYS

This project delves into the structural analysis of an off-road vehicle suspension system, employing cutting-edge software tools such as CATIA and ANSYS. Off-road vehicles necessitate robust suspension systems capable of withstanding harsh terrain conditions while ensuring vehicle stability, manoeuvrability, and passenger comfort. The study focuses on evaluating the structural integrity, performance, and behaviour of the suspension system under diverse load conditions and terrain scenarios. This project focuses on conducting a comprehensive structural analysis of an off-road vehicle suspension system using advanced engineering software tools, specifically CATIA and ANSYS. CATIA is a powerful computer-aided design (CAD) software known for its capabilities in creating detailed 3D models, while ANSYS is a leading finite element analysis (FEA) software used for simulating structural behaviour and analyzing mechanical systems. Keywords: suspension system, CATIA v5, ANSYS, iterations, FEA.

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Software engineering education in the era of conversational AI: current trends and future directions.

The developments in conversational AI raised urgent questions about the future direction of many aspects of society, including computing education. The first reactions to the fast-paced evolution of conversational agents were varied: Some announced "the end of programming," while others considered this "premature obituary of programming." Some adopted a defensive approach to detecting the use of conversational AI and avoiding an increase in plagiarism, while others questioned, "So what if ChatGPT wrote it?" Nevertheless, questions arise about whether computing education in its current form will still be relevant and fit for purpose in the era of conversational AI. Recognizing these diverse reactions to the advent of conversational AI, this paper aims to contribute to the ongoing discourse by exploring the current state through three perspectives in a dedicated literature review: adoption of conversational AI in (1) software engineering education specifically and (2) computing education in general, and (3) a comparison with software engineering practice. Our results show a gap between software engineering practice and higher education in the pace of adoption and the areas of use and generally identify preliminary research on student experience, teaching, and learning tools for software engineering.

FFEA software: Repeated early innovation

FFEA software: Repeated early innovation Dr Joanna Leng, Senior Research Software Engineering Fellow at the University of Leeds, discusses software with repeated early innovation using the example of the FFEA software. This article analyses the application of software engineering (SE) tools and practices and research software engineering (RSE) practices to the Fluctuating Finite Element Analysis (FFEA) software, a mesoscale modelling package. In 2016, I met the FFEA team to discuss improving their software. In 2020, I started working with them to develop a roadmap for the FFEA software by assessing their research interests and software.

Software Engineering: New Methodologies, Tools, and Best Practices in Software Development

Software engineering is a dynamic field that is always evolving to meet the demands of difficult software development projects. This journal article investigates contemporary software engineering methodologies, tools, and best practices in order to enhance the efficiency, quality, and dependability of software development processes. We discuss the most recent advancements in quality assurance, DevOps, agile software development, and automated testing. We also look at the role that cooperation, communication, and project management have in efficient software engineering methods. By highlighting these advances, this article aims to promote the delivery of high-quality software products and improve software development processes.

USE CASE TOOLS FOR IDENTIFICATION SOFTWARE REQUIREMENTS: STATE OF ART

The requirement analysis step of the Software Engineering Life Cycle (SELC) is the most crucial in order to ensure excellent quality. Besides, requirements specifications must be compared to the consistency, completeness, and correctness (3C's). Thus, dealing with Natural Language (NL) requirements makes this more challenging than usual., The problems that appear at this stage, if they are not detected or proceed, will lead to errors in the system and sometimes to the failure of the entire system; in addition to the effort required and the cost of maintenance will be large. Accordingly,to overcome these problems, differenttools have been developed and implemented to analyse the requirements through Natural Language Processing (NLP). These tools facilitate the process of eliciting requirements through Unified Modelling Language (UML) diagrams. Additionally, creating UML diagrams from NL specifications is a very difficult process. In particular, few efforts have been made in the work of extracting NL specifications, so these diagrams facilitate understanding and analysis of requirements and reduce the time of completion very significantly compared to the manual method. Using automated software engineering tools presents high-quality software that can be produced to help software developers and engineers. The main aim of this research is to concentrate onanalysing requirements based on the Use Case Diagram, which is a more popular one in UML diagram. Thus, this research will support other researchers in understanding and specifying the useful tools and mechanisms that can help them to analyse the requirements through Natural Language Processing (NLP) based UML diagrams

Concurrent Engineering Software Tools – A Trade-Off for efficient Learning in Blended Teaching Scenarios

Concurrent engineering is an approach to the development of complex systems that is characterised by direct communication between the disciplines involved. Key to this approach is the access to the most current design data by all participants at all times. This can be done via a dedicated software solution, for which both commercial and open-source software tools are available. How these tools influence the outcome of the class itself, has been discussed extensively in a separate publication. This contribution presents the experience that we gathered with different concurrent engineering software tools. The aim of this contribution is to offer other teachers and students some guideline for selecting a concurrent engineering software solution and implementing it in course work, in a way that using the tool itself does not become the central learning challenge of the course. The results might be of interest beyond university courses, as some requirements, like short times to get familiar with the software or certain interface requirements, also apply to other environments in research and development.

Improved Management of Issue Dependencies in Issue Trackers of Large Collaborative Projects

Issue trackers, such as Jira, have become the prevalent collaborative tools in software engineering for managing issues, such as requirements, development tasks, and software bugs. However, issue trackers inherently focus on the lifecycle of single issues, although issues have and express dependencies on other issues that constitute issue dependency networks in large complex collaborative projects. The objective of this study is to develop supportive solutions for the improved management of dependent issues in an issue tracker. This study follows the Design Science methodology, consisting of eliciting drawbacks and constructing and evaluating a solution and system. The study was carried out in the context of The Qt Company's Jira, which exemplifies an actively used, almost two-decade-old issue tracker with over 100,000 issues. The drawbacks capture how users operate with issue trackers to handle issue information in large, collaborative, and long-lived projects. The basis of the solution is to keep issues and dependencies as separate objects and automatically construct an issue graph. Dependency detections complement the issue graph by proposing missing dependencies, while consistency checks and diagnoses identify conflicting issue priorities and release assignments. Jira's plugin and service-based system architecture realize the functional and quality concerns of the system implementation. We show how to adopt the intelligent supporting techniques of an issue tracker in a complex use context and a large data-set. The solution considers an integrated and holistic system view, practical applicability and utility, and the practical characteristics of issue data, such as inherent incompleteness.

AStar: A modeling language for document-oriented geospatial data warehouses

A Geospatial Data Warehouse (GDW) is an extension of a traditional Data Warehouse that includes geospatial data in the decision-making processes. Several studies have proposed the use of document-oriented databases in a GDW as an alternative to relational databases. This is due to the ability of non-relational databases to scale horizontally, allowing for the storage and processing of large volumes of data. In this context, modeling the manner in which facts and dimensions are structured is important in order to understand, maintain, and evolve the Document-oriented GDW (DGDW) through visual analysis. However, to the best of our knowledge, there are no modeling languages that support the design of aggregated data as facts and dimensions, that can be represented as referenced or embedded documents, partitioned into one or more collections. To overcome this lack, we propose Aggregate Star (AStar), a Domain-Specific Modeling Language for designing DGDW logical schemas. AStar is defined from a concrete syntax (graphical notation), an abstract syntax (metamodel), and static semantics (well-formedness rules). In order to describe the semantics of the concepts defined in AStar, translational semantics map the graphical notation to the metamodel and the respective code, to define the schema in MongoDB (using JSON Schema). We evaluate the graphical notation using Physics of Notations (PoN), which provides a set of principles for designing cognitively effective visual notations. This evaluation revealed that AStar is in accordance with seven of the nine PoN principles, an adequate level of cognitive effectiveness. As a proof of concept, the metamodel and well-formedness rules were implemented in a prototype of Computer-Assisted Software Engineering tool, called AStarCASE. In its current version, AStarCASE can be used to design DGDW logical schemas and to generate their corresponding code in the form of JSON Schemas.

A probabilistic framework for mutation testing in deep neural networks

Context: Mutation Testing (MT) is an important tool in traditional Software Engineering (SE) white-box testing. It aims to artificially inject faults in a system to evaluate a test suite's capability to detect them, assuming that the test suite defects finding capability will then translate to real faults. If MT has long been used in SE, it is only recently that it started gaining the attention of the Deep Learning (DL) community, with researchers adapting it to improve the testability of DL models and improve the trustworthiness of DL systems. Objective: If several techniques have been proposed for MT, most of them neglected the stochasticity inherent to DL resulting from the training phase. Even the latest MT approaches in DL, which propose to tackle MT through a statistical approach, might give inconsistent results. Indeed, as their statistic is based on a fixed set of sampled training instances, it can lead to different results across instances set when results should be consistent for any instance. Methods: In this work, we propose a Probabilistic Mutation Testing (PMT) approach that alleviates the inconsistency problem and allows for a more consistent decision on whether a mutant is killed or not. Results: We show that PMT effectively allows a more consistent and informed decision on mutations through evaluation using three models and eight mutation operators used in previously proposed MT methods. We also analyze the trade-off between the approximation error and the cost of our method, showing that relatively small error can be achieved for a manageable cost. Conclusion: Our results showed the limitation of current MT practices in DNN and the need to rethink them. We believe PMT is the first step in that direction which effectively removes the lack of consistency across test executions of previous methods caused by the stochasticity of DNN training.

The Detection and Control over the offences of White Collar Crimes, Frauds and Hacking of information, by using effectively the relevant Software and Electronic Devices

In both developed and emerging nations, fraud, white-collar crime, and malpractices in small and medium-sized businesses, banking, and other sectors are on the rise. The criminals are carrying out their fraud-related offenses by using the most up-to-date information technology structures and similar electronic media to create unjust loss and harm in the fields of white-collar crimes, banking, business, and to persons. The goal of this research paper is to make these businesses understand the need of utilizing the most up-to-date, trustworthy, and legitimate software and the necessity of making their networks more secure from external threats. The thieves are able to accomplish their goals thanks to the usage of sophisticated features for tracking and hacking private information utilizing software and information technology resources. In essence, their tactics rely on deceit and dishonesty to harm others. In essence, their methods of operation rely on deceit and dishonesty to commit the crime. Employees of the affected organizations are frequently complicit in banking and white-collar scams, aiding and abetting outside criminals by transferring sensitive information. Parody, distortion, misrepresentation, twisting of the fact or truth, and concealing of actual information that is harmful to another person are all examples of fraud. The legislation's attempt to punish fraudsters seems extremely naive. There are many reasons why criminals flee, including the fact that their identities are frequently kept a secret. The criminals are able to move toward exoneration and freedom due to a lack of evidence, shoddy investigation, and naive prosecuting tactics. The fraudsters possess high-quality Software Engineering Tools and Electronic Equipment to commit offenses of such to harm entrepreneurs, financial organizations, and commercial banks by depriving and extracting information and money from the concerned accounts, Technically and operationally feasible and valuable suggestions for implementation are presented in this paper to safeguard the Networks of organizations.

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.

Consistent Design of Relational Databases using EERCASE

This article introduces EERCASE, a Computer Aided Software Engineering tool that is based on the best practices of the Model Driven Development paradigm to provide a consistent environment for relational database design. EERCASE follows the graphical notation of the Enhanced Entity–Relationship model according to Elmasri and Navathe, implements the EERMM metamodel to avoid syntactically invalid constructs, shows and describes static semantic errors, and generates data definition code that takes into account advanced structural validations. The theoretical and technical framework used for the implementation of EERCASE is discussed, with emphasis on the restrictive and informative validations performed by it. In addition, considering feedbacks on modeling errors and code generation, EERCASE is also presented as a computational environment that favors active learning.

Sentiment analysis tools in software engineering: A systematic mapping study

Software development is a collaborative task. Previous research has shown social aspects within development teams to be highly relevant for the success of software projects. A team’s mood has been proven to be particularly important. It is paramount for project managers to be aware of negative moods within their teams, as such awareness enables them to intervene. Sentiment analysis tools offer a way to determine the mood of a team based on textual communication. We aim to help developers or stakeholders in their choice of sentiment analysis tools for their specific purpose. Therefore, we conducted a systematic mapping study (SMS). We present the results of our SMS of sentiment analysis tools developed for or applied in the context of software engineering (SE). Our results summarize insights from 106 papers with respect to (1) the application domain, (2) the purpose, (3) the used data sets, (4) the approaches for developing sentiment analysis tools, (5) the usage of already existing tools, and (6) the difficulties researchers face. We analyzed in more detail which tools and approaches perform how in terms of their performance. According to our results, sentiment analysis is frequently applied to open-source software projects, and most approaches are neural networks or support-vector machines. The best performing approach in our analysis is neural networks and the best tool is BERT . Despite the frequent use of sentiment analysis in SE, there are open issues, e.g. regarding the identification of irony or sarcasm, pointing to future research directions. We conducted an SMS to gain an overview of the current state of sentiment analysis in order to help developers or stakeholders in this matter. Our results include interesting findings e.g. on the used tools and their difficulties. We present several suggestions on how to solve these identified problems.

Satisfiability modulo fuzzing: a synergistic combination of SMT solving and fuzzing

Programming languages and software engineering tools routinely encounter components that are difficult to reason on via formal techniques or whose formal semantics are not even available—third-party libraries, inline assembly code, SIMD instructions, system calls, calls to machine learning models, etc. However, often access to these components is available as input-output oracles—interfaces are available to query these components on certain inputs to receive the respective outputs. We refer to such functions as closed-box functions . Regular SMT solvers are unable to handle such closed-box functions. We propose Sādhak, a solver for SMT theories modulo closed-box functions. Our core idea is to use a synergistic combination of a fuzzer to reason on closed-box functions and an SMT engine to solve the constraints pertaining to the SMT theories. The fuzz and the SMT engines attempt to converge to a model by exchanging a rich set of interface constraints that are relevant and interpretable by them. Our implementation, Sādhak, demonstrates a significant advantage over the only other solver that is capable of handling such closed-box constraints: Sādhak solves 36.45% more benchmarks than the best-performing mode of this state-of-the-art solver and has 5.72x better PAR-2 score; on the benchmarks that are solved by both tools, Sādhak is (on an average) 14.62x faster.