Artificial intelligence and software engineering

Search-based software engineering (SBSE) consists of the application of metaheuristic search techniques—such as genetic algorithms, simulated annealing, hill climbing, and tabu search—to software engineering problems. This idea is based on the observation that some software engineering activities can be thought of as optimization problems, and on the fact that techniques providing exact solutions are, very often, unable to adequately scale to large, real-world software engineering problems. Define fitness functions to guide an automated search for solutions, rather than attempting to construct solutions from scratch by hand. SBSE moves the engineer up the abstraction chain, from the tedious and expensive role of detailed solution construction, to the potentially more productive and more cost-effective area of fitness function design. This approach has been repeatedly demonstrated to have successful applications across the software engineering life cycle from requirements and project planning to maintenance and testing. This special issue focuses on applications of search SBSE to the maintenance phase of the life cycle. After software testing 2, software maintenance is the most widely studied application area for SBSE research. Work on SBSE applications to problems in software maintenance can be traced back to the pioneering work of the Mancoridis et al. 3, which led to the development of the Bunch tool for software re-modularization 4. In the past five years there has been a significant increase in SBSE applications to software maintenance and evolution. Search-based techniques have been applied to post-development code improvement problems, such as object-oriented refactoring 5, 6, and imperative language slicing and transformation 7. Other applications are related to software engineering economics and project management problems, such as release planning 8-12, effort, and cost estimation 13-16, and project staffing and planning 17-20. Last, but not the least, SBSE has been applied to deal with service-oriented architectures, for example for services QoS-aware composition 21, service-level agreement negotiation 22, and testing 23. A complete overview of SBSE with a discussion of future research trends can be found in the recent International Conference on Software Engineering (ICSE) ‘Future of Software Engineering’ paper 24. This issue of the Journal of Software Maintenance and Evolution: Research and Practice features three outstanding papers, dealing with different aspects of software maintenance and evolution: release planning and staffing, re-factoring, and component substitution. The paper ‘Optimized Staffing for Product Releases—Focused Search and its Application at Chartwell Technologies’ by Puneet Kapur, An Ngo-The, Günther Ruhe, and Andrew Smith presents an approach for optimizing the staffing for product releases. The problem they solve can be viewed as part of the more general release planning problem, which assigns features to releases such that technical, resource, risk, and budget constraints are met. In particular, the genetic search-based approach the authors propose aims at assigning human resources to software development tasks, taking them from a pool of developers having different levels of skills. The applicability of the proposed approach is demonstrated with an industrial case study. The paper ‘Search-Based Refactoring: an Empirical Study’ by Mark O'Keffe and Mel Ó Cinnéide presents an approach to automatically re-factor, using search-based techniques, object-oriented systems that undergo repeated addition of functionality. Such addition can, very often, deteriorate the quality of the system's underlying design. Their re-factoring is guided by the QMOOD quality model 25, and the paper compares different optimization techniques, such as Genetic Algorithms, Simulated Annealing, and Multiple Ascent Hill-Climbing. The results of experiments carried out on five software systems suggest that the latter technique outperforms the others. The paper ‘Search-based Many-to-one Component Substitution’ by Nicolas Desnos, Marianne Huchard, Guy Tremblay, Christelle Urtado, and Sylvain Vauttier presents a search-based approach that allows the replacement of an obsolete component with a composition of multiple components. When a component becomes obsolete, the approach searches for a minimal assembly of other components, exposing their functionality by means of ports that preserves the behavior of the original component. The proposed approach has been evaluated on a set of randomly generated components. We hope that readers will enjoy this special issue and find the selected papers insightful and inspiring. We would like to thank all the authors who presented papers for this special issue: there were many good quality papers, but unfortunately we could accept only three of them. Last, but not the least, we would like to thank the reviewers who provided timely, detailed and constructive feedbacks to the authors. Without their precious help this special issue would not have been possible.

Artificial intelligence and recent advances in deep learning architectures, including transformer networks and large language models, change the way people think and act to solve problems. Software engineering, as an increasingly complex process to design, develop, test, deploy, and maintain large-scale software systems for solving real-world challenges, is profoundly affected by many revolutionary artificial intelligence tools in general and machine learning in particular. In this roadmap for artificial intelligence in software engineering, we highlight the recent deep impact of artificial intelligence on software engineering by discussing successful stories of applications of artificial intelligence to classic and new software development challenges. We identify the new challenges that the software engineering community has to address in the coming years to successfully apply artificial intelligence in software engineering, and we share our research roadmap toward the effective use of artificial intelligence in the software engineering profession, while still protecting fundamental human values. We spotlight three main areas that challenge the research in software engineering: the use of generative artificial intelligence and large language models for engineering large software systems, the need of large and unbiased datasets and benchmarks for training and evaluating deep learning and large language models for software engineering, and the need of a new code of digital ethics to apply artificial intelligence in software engineering.

Artificial Intelligence (AI) has a long tradition in software and requirements engineering (RE). Over the years, many AI techniques have been employed to represent and analyze requirements, ranging from knowledge representation and reasoning in the 1980s to the use of natural language (NL) processing, machine learning, and deep learning since the 2000s. AI techniques have been successfully applied in practice, for example, to manage large-volume requirements.<sup>1<sup>

The lack of human-friendly explanations from Artificial Intelligence (AI)—based decisions is a major concern for high stake applications. Towards mitigating the concern, Explainable AI (XAI) is an emerging area of research. In pre-modeling explainability, which is one of the notions of explainability, one can introduce explainability before training the model. One can also introduce explainability during training of the model or after training the model, in general, both of which are known as post-hoc explainability. Unfortunately, post-hoc explainability is not readily transparent and can be misleading, as it explains after the decision has been made and can be optimized to placate a subjective demand which is a form of bias. As a result, the explanation from it can be misleading even though it seems plausible. Explainability should incorporate knowledge from different domains such as philosophy, psychology, and cognitive science, so that the explanation is not just based on the researcher’s intuition of what constitutes a good explanation. Domain knowledge is an abstract, fuzzy, and high-level concept over the problem domain. For instance, in an image classification problem, the domain knowledge could be a dog has four legs, or a zebra has stripes, etc. However, the use of domain knowledge for explainability is under-focused and bound to problem-specific requirements. This chapter focuses on the notion of a pre-modeling explainability of AI-based “black box” models using domain knowledge. We demonstrate the collection and application of domain knowledge along with the quantification of explainability on an intrusion detection problem. Although AI-based Intrusion Detection Systems (IDS) provide accelerated speeds in intrusion detection, the response is still at a human speed when there is a human in the loop. The lack of explainability of an AI-based model is a key reason for this bottleneck as a human analyst has to understand the prediction before making the final decision towards mitigation of a problem. To mitigate this issue, in this chapter, we incorporate the CIA principle (i.e., domain knowledge) in an AI-based black box model for better explainability and generalizability of the model and demonstrate the process in detail. A major portion of this chapter is a compilation of our previously published work [1–4]. We start with a brief discussion of the problem, network intrusion detection and prevention, that we use to demonstrate our approach. Then we briefly introduced relevant domain knowledge and how it can be integrated into the overall architecture. In Sect. 3, we describe our experiments, followed by Sect. 4 which contains a discussion on results from the experiments. We conclude with limitations and future work in Sect. 5.

This position paper presents a synopsis of the imperative function artificial intelligence (AI) has partaken in software engineering (SE) as well as in software testing. In addition, the paper discusses how graphical user interface (GUI), and event driven software testing can derive benefits from the use of AI techniques. Artificial intelligence has significantly aided the process of the automation of different software process. The employment of AI in software testing is not novel, having played a crucial role in the automation of software testing since its innovation. The usage of AI techniques not only reduces the cost but it also guarantees better quality as well as thorough testing. GUI Testing can be considered as the most challenging area of software testing. Although the results are quite preliminary, but the application of different AI techniques for GUI testing has proven to produce ideal results. Nevertheless, the application of AI techniques in GUI testing, in comparison with software testing, which has procured much assistance by venturing with AI, leaves much to be desired.

Good software engineering methodology can be rendered ineffective by the presence of deadline pressure. A simple model is developed to explain the possible responses to deadline pressure and to show that there is a tendency to divert resources away from software engineering activities under such conditions. Management is identified as being responsible for ensuring that software engineering activities are given adequate resources. A managerial strategy for achieving these ends is suggested.

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects of this concept on performance of each AI technique are investigated. Finally, outcomes of the approaches are evaluated and compared by means of the mean absolute percentage error (MAPE). Results show that data preprocessing can significantly improve performance of the AI techniques. Meanwhile, ANFIS outcomes are more approximate to the actual loads than those of ANN and GA, so it can be considered as a suitable tool to deal with STLF problems.

Simple SummaryDistinguishing between benign vs. malignant bone lesions is often difficult on imaging. Many bone lesions are infrequent or rarely seen, and often only specialist radiologists have sufficient expertise to provide an accurate diagnosis. In addition, some benign bone tumours may exhibit potentially aggressive features that mimic malignant bone tumours, making the diagnosis even more difficult. The rapid development of artificial intelligence (AI) techniques has led to remarkable progress in image-recognition tasks, including the classification and characterization of various tumours. This study will review the most recent evidence for AI techniques in differentiating bone lesions on various imaging modalities using a systematic approach. Potential clinical applications of AI techniques for clinical diagnosis and management of bone lesions will also be discussed.An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Quality assessment and assurance constitute an important part of software engineering. The issues of software quality management are widely researched and approached from multiple perspectives and viewpoints. The introduction of a new paradigm in software development – namely Model Driven Development (MDD) and its variations (e.g., MDA [Model Driven Architecture], MDE [Model Driven Engineering], MBD [Model Based Development], MIC [Model Integrated Computing]) – raises new challenges in software quality management, and as such should be given special attention. In particular, the issues of early quality assessment, based on models at a high abstraction level, and building (or customizing the existing) prediction models for software quality based on model metrics are of central importance for the software engineering community.

Enterprise information systems(EIS) focus on dealing with the complex business logic of collecting, filtering, processing, and distributing data for improving productivity and service in our daily life. The successful development of enterprise information system is the labor-intensive activities in software engineering, it requires the sophisticated human efforts for requirements validation, system design, implementation and verification. Our previous work RM2PT can help to achieve a validated requirements model through automatically generating prototypes from requirements models to support incremental and rapid requirements validation. In this paper, we present a tool named RM2EIS to further alleviate the problem of system development by supporting automatically generate the back-end source code of enterprise information system from the validated requirements model, which are achieved by several round requirements validation in RM2PT. We demonstrate that RM2EIS can achieve higher quality code (+8.27%) with less time cost (-61.92%) than the manual development through 9 case studies. Overall, the results were satisfactory. The proposed approach can be further extended and applied for the EIS development in the software industry. The tool can be downloaded at https://rm2pt.com/advs/rm2eis and a demo video casting its features is available at https://www.youtube.com/watch?v=5Nde-JYezg4.

With the tremendous growth in the complexity of software development in the last 1015 years, the management of risks in software engineering activities is becoming an important and non-trivial issue from three perspectives: project, process and product. Therefore, researchers and practitioners are continually trying to find effective risk management approaches. In this paper, we briefly discuss the principles of risk management in software engineering, review some of the process models popular in the software engineering community, and finally discuss some of the important works conducted recently in this area in the last five years. While writing the paper, we have focused mostly on project, and process-based risks and not on product risks.

Artificial Intelligence (AI) is becoming a fundamental component of software engineering, both in terms of using AI applications in software engineering (AI for SE) and using software engineering to develop AI systems (SE for AI).The SWEBOK highlights relevant AI knowledge areas, but in practice, AI impacts all aspects of software engineering.As a result, software engineering education needs to take account not only of a set of knowledge areas but also a foundational set of AI literacies that inform students about the wider and deeper implications of AI systems.This article addresses this challenge by mapping knowledge areas from the SWEBOK to the Scaffolded AI Literacy (SAIL) Framework.Using some example mappings from knowledge areas to AI literacies, followed by illustrative examples of teaching scenarios, it explores how SE knowledge areas can be supported and enhanced by developing AI literacies.

Along with the more public debate about the benefits the Semantic Web is - or isn't - delivering to the Internet, a related undercurrent of discussion is growing about software engineering's future. In the vanguard of this discussion, some researchers and software engineers are advocating that Semantic Web technologies should be key components of the next paradigm of software development. These advocates, however, are discovering that the ongoing debate about the Semantic Web's ultimate usefulness is being fought over the user experience - whether or not the principles behind the Semantic Web are just too complex to be truly useful to the bulk of Internet users.

It is no doubt that now a day’s Artificial intelligence (AI) is anemergent technology in the digital marketing industry. Commercial Artificial Intelligence (AI) is an important topic for research. Artificial intelligence is growing quickly more sensitive and popular in the digital marketing field due to the capacity of advertisers to employ it efficiently. In order to provide the capability of the highest level of originality and efficiency in digital marketing ground, artificial intelligence techniques are more fruitful for customers, marketers and, retailers. This paper depicts the impact of artificial intelligence techniques on advertising on a diminutive and average scale in the digital marketing field. Ad targeting, predictive analysis, voice search are different artificial intelligence techniques for digital marketing purposes throughout the consumer life cycle. The topic of Artificial Intelligence (AI) in digital marketing itself can be outsized enough to verifyeliminating the area of research into precise application and application frameworks.However, our objective in this paper is to provide a comparative analysis of the digital marketing field using artificial intelligence techniques and to propose some specific guidelines based on different parameters, tools, and frameworks.Through this paper, we have observed thelast five years (2016-2021) research trends which reflect that out of different parameters, customer relationship and promotional activity are the most widely used parameter in the digital marketing field. Another significant point is, most of the research identifies that artificial intelligence is a compulsory technique used in the modern digital marketing field. Some data collection and data analysis tools are also being used in this field using artificial intelligence techniques. Our research methods try to get a complete view on artificial intelligence (AI) Marketing were multifaceted but were very focused on secondary research sources. Finally in this paper, we propose some recommendationsthat will offer better Artificial Intelligence (AI) implementation in the digital marketing field.