Information and Software Technology最新文献

{"title":"Dynamic robustness evaluation for automated model selection in operation","authors":"Jin Zhang ,&nbsp;Jingyue Li ,&nbsp;Zhirong Yang","doi":"10.1016/j.infsof.2024.107603","DOIUrl":"10.1016/j.infsof.2024.107603","url":null,"abstract":"<div><h3>Context:</h3><div>The increasing use of artificial neural network (ANN) classifiers in systems, especially safety-critical systems (SCSs), requires ensuring their robustness against out-of-distribution (OOD) shifts in operation, which are changes in the underlying data distribution from the data training the classifier. However, measuring the robustness of classifiers in operation with only unlabeled data is challenging. Additionally, machine learning engineers may need to compare different models or versions of the same model and switch to an optimal version based on their robustness.</div></div><div><h3>Objective:</h3><div>This paper explores the problem of dynamic robustness evaluation for automated model selection. We aim to find efficient and effective metrics for evaluating and comparing the robustness of multiple ANN classifiers using unlabeled operational data.</div></div><div><h3>Methods:</h3><div>To quantitatively measure the differences between the model outputs and assess robustness under OOD shifts using unlabeled data, we choose distance-based metrics. An empirical comparison of five such metrics, suitable for higher-dimensional data like images, is performed. The selected metrics include Wasserstein distance (WD), maximum mean discrepancy (MMD), Hellinger distance (HL), Kolmogorov–Smirnov statistic (KS), and Kullback–Leibler divergence (KL), known for their efficacy in quantifying distribution differences. We evaluate these metrics on 20 state-of-the-art models (ten CIFAR10-based models, five CIFAR100-based models, and five ImageNet-based models) from a widely used robustness benchmark (<strong>RobustBench</strong>) using data perturbed with various types and magnitudes of corruptions to mimic real-world OOD shifts.</div></div><div><h3>Results:</h3><div>Our findings reveal that the WD metric outperforms others when ranking multiple ANN models for CIFAR10- and CIFAR100-based models, while the KS metric demonstrates superior performance for ImageNet-based models. MMD can be used as a reliable second option for both datasets.</div></div><div><h3>Conclusion:</h3><div>This study highlights the effectiveness of distance-based metrics in ranking models’ robustness for automated model selection. It also emphasizes the significance of advancing research in dynamic robustness evaluation.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"178 ","pages":"Article 107603"},"PeriodicalIF":3.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey on Cryptoagility and Agile Practices in the light of quantum resistance","authors":"Lodovica Marchesi ,&nbsp;Michele Marchesi ,&nbsp;Roberto Tonelli","doi":"10.1016/j.infsof.2024.107604","DOIUrl":"10.1016/j.infsof.2024.107604","url":null,"abstract":"<div><h3>Context:</h3><div>Crypto-agility, a name that stems from agile methodologies for software development, means the ability to modify quickly and securely cryptographic algorithms in the event of a compromise. The advent of quantum computing poses existential threats to current cryptography, having the power to breach current cryptography systems.</div></div><div><h3>Objective:</h3><div>We investigated whether and to what extent agile practices for software development are suited to support crypto-agility, or not. In particular, we discuss their usefulness in the context of substituting current algorithms with quantum-resistant ones.</div></div><div><h3>Method:</h3><div>First, we analyzed the literature to define a subset of 15 agile practices potentially relevant to cryptographic software development. Then, we developed a questionnaire to assess the suitability of agile practices for obtaining crypto-agility. We performed a Web search of relevant documents about crypto-agility and quantum resistance and sent their authors the questionnaire. We also sent the questionnaire to cybersecurity officers of four Italian firms. We analyzed and discussed the responses to 32 valid questionnaires.</div></div><div><h3>Results:</h3><div>The respondents’ affiliations are evenly distributed between researchers and developers. Most of them are active, or somehow active, in quantum-resistant cryptography and use agile methods. Most of the agile practices are deemed to be quite useful, or very useful to get crypto-agility, the most effective being Continuous Integration and Coding Standards; the least appreciated is Self-organizing Team.</div></div><div><h3>Conclusion:</h3><div>According to researchers and developers working in the field, the safe transition of cryptographic algorithms to quantum-resistant ones can benefit from the adoption of many agile practices. Further software engineering research is needed to integrate agile practices in more formal cryptographic software development processes.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"178 ","pages":"Article 107604"},"PeriodicalIF":3.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E-code: Mastering efficient code generation through pretrained models and expert encoder group","authors":"Yue Pan ,&nbsp;Chen Lyu ,&nbsp;Zhenyu Yang ,&nbsp;Lantian Li ,&nbsp;Qi Liu ,&nbsp;Xiuting Shao","doi":"10.1016/j.infsof.2024.107602","DOIUrl":"10.1016/j.infsof.2024.107602","url":null,"abstract":"<div><h3>Context:</h3><div>With the waning of Moore’s Law, the software industry is placing increasing importance on finding alternative solutions for continuous performance enhancement. The significance and research results of software performance optimization have been on the rise in recent years, especially with the advancement propelled by <strong>L</strong>arge <strong>L</strong>anguage <strong>M</strong>odel<strong>s</strong> (LLMs). However, traditional strategies for rectifying performance flaws have shown significant limitations at the competitive code efficiency optimization level, and research on this topic is surprisingly scarce.</div></div><div><h3>Objective:</h3><div>This study aims to address the research gap in this domain, offering practical solutions to the various challenges encountered. Specifically, we have overcome the constraints of traditional performance error rectification strategies and developed a <strong>L</strong>anguage <strong>M</strong>odel (LM) tailored for the competitive code efficiency optimization realm.</div></div><div><h3>Methods:</h3><div>We introduced E-code, an advanced program synthesis LM. Inspired by the recent success of expert LMs, we designed an innovative structure called the Expert Encoder Group. This structure employs multiple expert encoders to extract features tailored for different input types. We assessed the performance of E-code against other leading models on a competitive dataset and conducted in-depth ablation experiments.</div></div><div><h3>Results:</h3><div>Upon systematic evaluation, E-code achieved a 54.98% improvement in code efficiency, significantly outperforming other advanced models. In the ablation experiments, we further validated the significance of the expert encoder group and other components within E-code.</div></div><div><h3>Conclusion:</h3><div>The research findings indicate that the expert encoder group can effectively handle various inputs in efficiency optimization tasks, significantly enhancing the model’s performance. In summary, this study paves new avenues for developing systems and methods to assist programmers in writing efficient code.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"178 ","pages":"Article 107602"},"PeriodicalIF":3.8,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic review on the current state of computer-supported argumentation learning systems","authors":"Laura Sinikallio ,&nbsp;Lili Aunimo ,&nbsp;Tomi Männistö","doi":"10.1016/j.infsof.2024.107598","DOIUrl":"10.1016/j.infsof.2024.107598","url":null,"abstract":"<div><h3>Context:</h3><div>Argumentation is a fundamental part of learning, communication and problem-solving not only in software engineering but all education. Teaching argumentation is a long-standing practice, and with the advance of digital learning, it, too, has been transitioning to an online format.</div></div><div><h3>Objective:</h3><div>As computer-supported argumentation learning progresses, other learning domains have much to learn from it on how to enable argumentation and reasoning in automated and scalable online learning solutions.</div></div><div><h3>Methods:</h3><div>To review the current state of the field, we conducted a systematic literature review on the last decade of academic research and design on computer-supported argumentation learning systems. We reviewed and summarised the central aspects and approaches of reported systems.</div></div><div><h3>Results:</h3><div>We reviewed 34 different argumentation learning tools. The review showed that approaches to computer-supported argumentation vary significantly in many aspects, e.g., argumentation theory, learning task types and collaboration status. However, the use of argumentation graphs is quite common. Most modern tools seem to embrace the role of feedback in learning.</div></div><div><h3>Conclusions:</h3><div>The role of individual learning has risen in computer-supported argumentation learning. This is in opposition to previous predictions and statements on the role of collaborative learning of argumentation. Automated feedback has, on the other hand, become commonplace in collaborative and individual-use argumentation learning tools. The modern generation of argumentation teaching tools is Web-based but recently we have also seen the emergence of mobile-based solutions.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"178 ","pages":"Article 107598"},"PeriodicalIF":3.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling efficient and low-effort decentralized federated learning with the EdgeFL framework","authors":"Hongyi Zhang ,&nbsp;Jan Bosch ,&nbsp;Helena Holmström Olsson","doi":"10.1016/j.infsof.2024.107600","DOIUrl":"10.1016/j.infsof.2024.107600","url":null,"abstract":"<div><h3>Context:</h3><div>Federated Learning (FL) has gained prominence as a solution for preserving data privacy in machine learning applications. However, existing FL frameworks pose challenges for software engineers due to implementation complexity, limited customization options, and scalability issues. These limitations prevent the practical deployment of FL, especially in dynamic and resource-constrained edge environments, preventing its widespread adoption.</div></div><div><h3>Objective:</h3><div>To address these challenges, we propose EdgeFL, an efficient and low-effort FL framework designed to overcome centralized aggregation, implementation complexity and scalability limitations. EdgeFL applies a decentralized architecture that eliminates reliance on a central server by enabling direct model training and aggregation among edge nodes, which enhances fault tolerance and adaptability to diverse edge environments.</div></div><div><h3>Methods:</h3><div>We conducted experiments and a case study to demonstrate the effectiveness of EdgeFL. Our approach focuses on reducing weight update latency and facilitating faster model evolution on edge devices.</div></div><div><h3>Results:</h3><div>Our findings indicate that EdgeFL outperforms existing FL frameworks in terms of learning efficiency and performance. By enabling quicker model evolution on edge devices, EdgeFL enhances overall efficiency and responsiveness to changing data patterns.</div></div><div><h3>Conclusion:</h3><div>EdgeFL offers a solution for software engineers and companies seeking the benefits of FL, while effectively overcoming the challenges and privacy concerns associated with traditional FL frameworks. Its decentralized approach, simplified implementation, combined with enhanced customization and fault tolerance, make it suitable for diverse applications and industries.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"178 ","pages":"Article 107600"},"PeriodicalIF":3.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal reasoning in Software Quality Assurance: A systematic review","authors":"Luca Giamattei,&nbsp;Antonio Guerriero,&nbsp;Roberto Pietrantuono,&nbsp;Stefano Russo","doi":"10.1016/j.infsof.2024.107599","DOIUrl":"10.1016/j.infsof.2024.107599","url":null,"abstract":"<div><h3>Context:</h3><div>Software Quality Assurance (SQA) is a fundamental part of software engineering to ensure stakeholders that software products work as expected after release in operation. Machine Learning (ML) has proven to be able to boost SQA activities and contribute to the development of quality software systems. In this context, <em>Causal Reasoning</em> is gaining increasing interest as a methodology to go beyond a purely data-driven approach by exploiting the use of causality for more effective SQA strategies.</div></div><div><h3>Objective:</h3><div>Provide a broad and detailed overview of the use of causal reasoning for SQA activities, in order to support researchers to access this research field, identifying room for application, main challenges and research opportunities.</div></div><div><h3>Methods:</h3><div>A systematic review of the scientific literature on causal reasoning for SQA. The study has found, classified, and analyzed 86 articles, according to established guidelines for software engineering secondary studies.</div></div><div><h3>Results:</h3><div>Results highlight the primary areas within SQA where causal reasoning has been applied, the predominant methodologies used, and the level of maturity of the proposed solutions. Fault localization is the activity where causal reasoning is more exploited, especially in the web services/microservices domain, but other tasks like testing are rapidly gaining popularity. Both causal inference and causal discovery are exploited, with the Pearl’s graphical formulation of causality being preferred, likely due to its intuitiveness. Tools to favor their application are appearing at a fast pace — most of them after 2021.</div></div><div><h3>Conclusions:</h3><div>The findings show that causal reasoning is a valuable means for SQA tasks with respect to multiple quality attributes, especially during V&amp;V, evolution and maintenance to ensure reliability, while it is not yet fully exploited for phases like requirements engineering and design. We give a picture of the current landscape, pointing out exciting possibilities for future research.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"178 ","pages":"Article 107599"},"PeriodicalIF":3.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting and Explaining Python Name Errors","authors":"Jiawei Wang ,&nbsp;Li Li ,&nbsp;Kui Liu ,&nbsp;Xiaoning Du","doi":"10.1016/j.infsof.2024.107592","DOIUrl":"10.1016/j.infsof.2024.107592","url":null,"abstract":"<div><div>Python has become one of the most popular programming languages nowadays but has not received enough attention from the software engineering community. Many errors, either fixed or not yet, have been scattered in the lifetime of Python projects, including popular Python libraries that have already been reused. NameError is among one of those errors that are widespread in the Python community, as confirmed in our empirical study. Yet, our community has not put effort into helping developers mitigate its introductions. To fill this gap, we propose in this work a static analysis-based approach called <em>DENE</em> (short for <strong>D</strong>etecting and <strong>E</strong>xplaining <strong>N</strong>ame <strong>E</strong>rrors) to automatically detect and explain name errors in Python projects. To this end, <em>DENE</em> builds control-flow graphs for Python projects and leverages a scope-aware reaching definition analysis to locate identifiers that may cause name errors at runtime and report their locations. Experimental results on carefully crafted ground truth demonstrate that <em>DENE</em> is effective in detecting name errors in real-world Python projects. The results also confirm that unknown name errors are still widely presented in popular Python projects and libraries, and the outputs of <em>DENE</em> can indeed help developers understand why the name errors are flagged as such.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"178 ","pages":"Article 107592"},"PeriodicalIF":3.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Better together: Automated app review analysis with deep multi-task learning","authors":"Yawen Wang ,&nbsp;Junjie Wang ,&nbsp;Hongyu Zhang ,&nbsp;Xuran Ming ,&nbsp;Qing Wang","doi":"10.1016/j.infsof.2024.107597","DOIUrl":"10.1016/j.infsof.2024.107597","url":null,"abstract":"<div><h3>Context:</h3><div>User reviews of mobile apps provide an important communication channel between developers and users. Existing approaches to automated app review analysis mainly focus on one task (e.g., bug classification task, information extraction task, etc.) at a time, and are often constrained by the manually defined patterns and the ignorance of the correlations among the tasks. Recently, multi-task learning (MTL) has been successfully applied in many scenarios, with the potential to address the limitations associated with app review mining tasks.</div></div><div><h3>Objective:</h3><div>In this paper, we propose <span>MABLE</span>, a deep MTL-based and semantic-aware approach, to improve app review analysis by exploiting task correlations.</div></div><div><h3>Methods:</h3><div><span>MABLE</span> jointly identifies the types of involved bugs reported in the review and extracts the fine-grained features where bugs might occur. It consists of three main phases: (1) data preparation phase, which prepares data to allow data sharing beyond single task learning; (2) model construction phase, which employs a BERT model as the shared representation layer to capture the semantic meanings of reviews, and task-specific layers to model two tasks in parallel; (3) model training phase, which enables eavesdropping by shared loss function between the two related tasks.</div></div><div><h3>Results:</h3><div>Evaluation results on six apps show that <span>MABLE</span> outperforms ten commonly-used and state-of-the-art baselines, with the precision of 79.76% and the recall of 79.24% for classifying bugs, and the precision of 79.83% and the recall of 80.33% for extracting problematic app features. The MTL mechanism improves the F-measure of two tasks by 3.80% and 4.63%, respectively.</div></div><div><h3>Conclusion:</h3><div>The proposed approach provides a novel and effective way to jointly learn two related review analysis tasks, and sheds light on exploring other review mining tasks.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"177 ","pages":"Article 107597"},"PeriodicalIF":3.8,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A family of experiments to quantify the benefits of adopting WebDriverManager and Selenium-Jupiter","authors":"Maurizio Leotta ,&nbsp;Boni García ,&nbsp;Filippo Ricca","doi":"10.1016/j.infsof.2024.107595","DOIUrl":"10.1016/j.infsof.2024.107595","url":null,"abstract":"<div><h3>Context:</h3><div>While test automation offers numerous benefits, it also introduces significant challenges. Two challenges that developers and testers face on a daily basis, particularly when using Selenium WebDriver to test web applications, are driver management (involving tasks such as version identification, download, installation, and maintenance) and management of test lifecycle phases (using specific test libraries, as for example JUnit, and inserting annotations into the code). These manual tasks make test suite development particularly tedious, error-prone, and expensive. Recently, to ease the burden on developers and testers, some Java libraries have been proposed, called <em>WebDriverManager</em> and <em>Selenium-Jupiter</em>, capable of automatically carrying out the driver management process for Selenium WebDriver and simplifying the development of test suites. These libraries appear to be very promising but until now no one has experimentally evaluated their effectiveness.</div></div><div><h3>Objective:</h3><div>To investigate the effectiveness of <em>WebDriverManager</em> and <em>Selenium-Jupiter</em> in reducing driver management times and boilerplate code.</div></div><div><h3>Method:</h3><div>We designed and conducted a family of experiments (three for <em>WebDriverManager</em> and two for <em>Selenium-Jupiter</em>) with 104 master student participants from the University of Genoa, Italy (across academic years 2021/2022 and 2022/2023) and nine professional participants.</div></div><div><h3>Results:</h3><div>Results indicate that the adoption of Selenium WebDriver with <em>WebDriverManager</em> significantly reduces setup time for multi-browser test suites from 33% to 50% (depending on the tester experience). Additionally, <em>Selenium-Jupiter</em> reduces test suite development time significantly (20% on average). Although it also decreases total code length, the reduction is relatively small compared to overall code length.</div></div><div><h3>Conclusion:</h3><div><em>WebDriverManager</em> and <em>Selenium-Jupiter</em> can be seen as valuable solutions for enhancing testers’ productivity by shortening the time needed to develop test suites and minimizing the amount of code to write.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"178 ","pages":"Article 107595"},"PeriodicalIF":3.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strategic digital product management: Nine approaches","authors":"Helena Holmström Olsson ,&nbsp;Jan Bosch","doi":"10.1016/j.infsof.2024.107594","DOIUrl":"10.1016/j.infsof.2024.107594","url":null,"abstract":"<div><h3>Context:</h3><div>The role of product management (PM) is key for building, implementing and managing software-intensive systems. Whereas engineering is concerned with how to build systems, PM is concerned with ‘what’ to build and ‘why’ we should build the product. The role of PM is recognized as critical for the success of any product. However, few studies explore how the role of PM is changing due to recent trends that come with digitalization and digital transformation.</div></div><div><h3>Objectives:</h3><div>Although there is prominent research on PM, few studies explore how this role is changing due to the digital transformation of the software-intensive industry. In this paper, we study how trends such as DevOps and short feedback loops, data and artificial intelligence (AI), as well as the emergence of digital ecosystems, are changing current product management practices.</div></div><div><h3>Methods:</h3><div>This study employs a qualitative approach using multi-case study research as the method. For our research, we selected five case companies in the software-intensive systems domain. Through workshop sessions, frequent meetings and interviews, we explore how DevOps and short feedback loops, data and artificial intelligence (AI), and digital ecosystems challenge current PM practices.</div></div><div><h3>Results:</h3><div>Our study yielded an in-depth understanding of how digital transformation of the software-intensive systems industry is changing current PM practices. We present empirical results from workshops and from interviews in which case company representatives share their insights on how software, data and AI impact current PM practices. Based on these results, we present a framework organized along two dimensions, i.e. a certainty dimension and an approach dimension. The framework helps structure the approaches product managers can employ to select and prioritize development of new functionality.</div></div><div><h3>Contributions:</h3><div>The contribution of this paper is a framework for ‘Strategic Digital Product Management’ (SDPM). The framework outlines nine approaches that product managers can employ to maximize the return on investment (RoI) of R&amp;D using new digital technologies.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"177 ","pages":"Article 107594"},"PeriodicalIF":3.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}