Information and Software Technology最新文献

{"title":"GitHub marketplace for automation and innovation in software production","authors":"SK. Golam Saroar,&nbsp;Waseefa Ahmed,&nbsp;Elmira Onagh,&nbsp;Maleknaz Nayebi","doi":"10.1016/j.infsof.2024.107522","DOIUrl":"10.1016/j.infsof.2024.107522","url":null,"abstract":"<div><h3>Context:</h3><p>GitHub, renowned for facilitating collaborative code version control and software production in software teams, expanded its services in 2017 by introducing GitHub Marketplace. This online platform hosts automation tools to assist developers with the production of their GitHub-hosted projects, and it has become a valuable source of information on the tools used in the Open Source Software (OSS) community.</p></div><div><h3>Objective:</h3><p>In this exploratory study, we introduce GitHub Marketplace as a software marketplace by exploring the Characteristics, Features, and Policies of the platform comprehensively, identifying common themes in production automation. Further, we explore popular tools among practitioners and researchers and highlight disparities in the approach to these tools between industry and academia.</p></div><div><h3>Method:</h3><p>We adopted the conceptual framework of software app stores from previous studies and used that to examine 8,318 automated production tools (440 Apps and 7,878 Actions) across 32 categories on GitHub Marketplace. We explored and described the policies of this marketplace as a unique platform where developers share production tools for the use of other developers. Furthermore, we conducted a systematic mapping of 515 research papers published from 2000 to 2021 and compared open-source academic production tools with those available in the marketplace.</p></div><div><h3>Results:</h3><p>We found that although some of the automation topics in literature are widely used in practice, they have yet to align with the state-of-practice for automated production. We discovered that practitioners often use automation tools for tasks like “Continuous Integration” and “Utilities”, while researchers tend to focus more on “Code Quality” and “Testing”.</p></div><div><h3>Conclusion:</h3><p>Our study illuminates the landscape of open-source tools for automation production. We also explored the disparities between industry trends and researchers’ priorities. Recognizing these distinctions can empower researchers to build on existing work and guide practitioners in selecting tools that meet their specific needs. Bridging this gap between industry and academia helps with further innovation in the field and ensures that research remains pertinent to the evolving challenges in software production.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"175 ","pages":"Article 107522"},"PeriodicalIF":3.8,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001277/pdfft?md5=a5f34bd55e7fac78519b3f67fd64a5c6&pid=1-s2.0-S0950584924001277-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693361","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":"Fine-tuning and prompt engineering for large language models-based code review automation","authors":"Chanathip Pornprasit,&nbsp;Chakkrit Tantithamthavorn","doi":"10.1016/j.infsof.2024.107523","DOIUrl":"10.1016/j.infsof.2024.107523","url":null,"abstract":"<div><h3>Context:</h3><p>The rapid evolution of Large Language Models (LLMs) has sparked significant interest in leveraging their capabilities for automating code review processes. Prior studies often focus on developing LLMs for code review automation, yet require expensive resources, which is infeasible for organizations with limited budgets and resources. Thus, fine-tuning and prompt engineering are the two common approaches to leveraging LLMs for code review automation.</p></div><div><h3>Objective:</h3><p>We aim to investigate the performance of LLMs-based code review automation based on two contexts, i.e., when LLMs are leveraged by fine-tuning and prompting. Fine-tuning involves training the model on a specific code review dataset, while prompting involves providing explicit instructions to guide the model’s generation process without requiring a specific code review dataset.</p></div><div><h3>Methods:</h3><p>We leverage model fine-tuning and inference techniques (i.e., zero-shot learning, few-shot learning and persona) on LLMs-based code review automation. In total, we investigate 12 variations of two LLMs-based code review automation (i.e., GPT-3.5 and Magicoder), and compare them with the Guo et al.’s approach and three existing code review automation approaches (i.e., CodeReviewer, TufanoT5 and D-ACT).</p></div><div><h3>Results:</h3><p>The fine-tuning of GPT 3.5 with zero-shot learning helps GPT-3.5 to achieve 73.17%–74.23% higher EM than the Guo et al.’s approach. In addition, when GPT-3.5 is not fine-tuned, GPT-3.5 with few-shot learning achieves 46.38%–659.09% higher EM than GPT-3.5 with zero-shot learning.</p></div><div><h3>Conclusions:</h3><p>Based on our results, we recommend that (1) LLMs for code review automation should be fine-tuned to achieve the highest performance.; and (2) when data is not sufficient for model fine-tuning (e.g., a cold-start problem), few-shot learning without a persona should be used for LLMs for code review automation. Our findings contribute valuable insights into the practical recommendations and trade-offs associated with deploying LLMs for code review automation.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"175 ","pages":"Article 107523"},"PeriodicalIF":3.8,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001289/pdfft?md5=526a4187620c208e9aedacd19f66db65&pid=1-s2.0-S0950584924001289-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728781","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":"Towards assessing the quality of knowledge graphs via differential testing","authors":"Jiajun Tan ,&nbsp;Dong Wang ,&nbsp;Jingyu Sun ,&nbsp;Zixi Liu ,&nbsp;Xiaoruo Li ,&nbsp;Yang Feng","doi":"10.1016/j.infsof.2024.107521","DOIUrl":"10.1016/j.infsof.2024.107521","url":null,"abstract":"<div><p>Knowledge graphs (KG) can aggregate data and make information resources easier to calculate and understand. With tremendous advancements in knowledge graphs, they have been incorporated into plenty of software systems to assist various tasks. However, while KGs determine the performance of downstream software systems, their quality is often measured by the accuracy of test data. Considering the limitation of accessible high-quality test data, an automated quality assessment technique could fundamentally improve the testing efficiency of KG-driven software systems and save plenty of manual labeling resources.</p><p>In this paper, we propose an automated approach to quantify the quality of KGs via differential testing. It first constructs multiple Knowledge Graph Embedding Models (KGEM) and conducts head prediction tasks on models. Then, it can produce a differential score that reflects the quality of KGs by comparing the proximity of output results. To validate the effectiveness of this approach, we experiment with four open-sourced knowledge graphs. The experiment results show that our approach is capable of accurately evaluating the quality of KGs and producing reliable results on different datasets. Moreover, we compared our method with existing methods and achieved certain advantages. The potential usefulness of our approach sheds light on the development of various KG-driven software systems.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107521"},"PeriodicalIF":3.8,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622752","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":"CausalOps — Towards an industrial lifecycle for causal probabilistic graphical models","authors":"Robert Maier ,&nbsp;Andreas Schlattl ,&nbsp;Thomas Guess ,&nbsp;Jürgen Mottok","doi":"10.1016/j.infsof.2024.107520","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107520","url":null,"abstract":"<div><h3>Context:</h3><p>Causal probabilistic graph-based models have gained widespread utility, enabling the modeling of cause-and-effect relationships across diverse domains. With their rising adoption in new areas, such as safety analysis of complex systems, software engineering, and machine learning, the need for an integrated lifecycle framework akin to DevOps and MLOps has emerged. Currently, such a reference for organizations interested in employing causal engineering is missing. This lack of guidance hinders the incorporation and maturation of causal methods in the context of real-life applications.</p></div><div><h3>Objective:</h3><p>This work contextualizes causal model usage across different stages and stakeholders and outlines a holistic view of creating and maintaining them within the process landscape of an organization.</p></div><div><h3>Methods:</h3><p>A novel lifecycle framework for causal model development and application called CausalOps is proposed. By defining key entities, dependencies, and intermediate artifacts generated during causal engineering, a consistent vocabulary and workflow model to guide organizations in adopting causal methods are established.</p></div><div><h3>Results:</h3><p>Based on the early adoption of the discussed methodology to a real-life problem within the automotive domain, an experience report underlining the practicability and challenges of the proposed approach is discussed.</p></div><div><h3>Conclusion:</h3><p>It is concluded that besides current technical advancements in various aspects of causal engineering, an overarching lifecycle framework that integrates these methods into organizational practices is missing. Although diverse skills from adjacent disciplines are widely available, guidance on how to transfer these assets into causality-driven practices still need to be addressed in the published literature. CausalOps’ aim is to set a baseline for the adoption of causal methods in practical applications within interested organizations and the causality community.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107520"},"PeriodicalIF":3.8,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001253/pdfft?md5=f05b4874cd2ba66373c469b0036d234f&pid=1-s2.0-S0950584924001253-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540923","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":"Are your apps accessible? A GCN-based accessibility checker for low vision users","authors":"Mengxi Zhang ,&nbsp;Huaxiao Liu ,&nbsp;Shenning Song ,&nbsp;Chunyang Chen ,&nbsp;Pei Huang ,&nbsp;Jian Zhao","doi":"10.1016/j.infsof.2024.107518","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107518","url":null,"abstract":"<div><h3>Context:</h3><p>Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision users in interacting with Graphical User Interfaces (GUIs). Although GUI accessibility scanning tools exist, most of them perform rule-based check relying on complex GUI hierarchies. This might make them detect invisible redundant information, cannot handle small deviations, omit similar components, and is hard to extend.</p></div><div><h3>Objective:</h3><p>In this paper, we propose a novel approach, named ALVIN (Accessibility Checker for Low Vision), which represents the GUI as a graph and adopts the Graph Convolutional Neural Networks (GCN) to label inaccessible components.</p></div><div><h3>Method:</h3><p>ALVIN removes invisible views to prevent detecting redundancy and uses annotations from low vision users to handle small deviations. Also, the GCN model could consider the relations between GUI components, connecting similar components and reducing the possibility of omission. ALVIN only requires users to annotate the relevant dataset when detecting new kinds of issues.</p></div><div><h3>Results:</h3><p>Our experiments on 48 apps demonstrate the effectiveness of ALVIN, with precision of 83.5%, recall of 78.9%, and F1-score of 81.2%, outperforming baseline methods. In RQ2, the usefulness is verified through 20 issues submitted to open-source apps. The RQ3 also illustrates the GCN model is better than other models.</p></div><div><h3>Conclusion:</h3><p>To summarize, our proposed approach can effectively detect accessibility issues in GUIs for low vision users, thereby guiding developers in fixing them efficiently.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107518"},"PeriodicalIF":3.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486232","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":"Towards antifragility of cloud systems: An adaptive chaos driven framework","authors":"Joseph S. Botros ,&nbsp;Lamis F. Al-Qora'n ,&nbsp;Amro Al-Said Ahmad","doi":"10.1016/j.infsof.2024.107519","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107519","url":null,"abstract":"<div><h3>Context</h3><p>Unlike resilience, antifragility describes systems that get stronger rather than weaker under stress and chaos. Antifragile systems have the capacity to overcome stressors and come out stronger, whereas resilient systems are focused on their capacity to return to their previous state following a failure. As technology environments become increasingly complex, there is a great need for developing software systems that can benefit from failures while continuously improving. Most applications nowadays operate in cloud environments. Thus, with this increasing adoption of Cloud-Native Systems they require antifragility due to their distributed nature.</p></div><div><h3>Objective</h3><p>The paper proposes UNFRAGILE framework, which facilitates the transformation of existing systems into antifragile systems. The framework employs chaos engineering to introduce failures incrementally and assess the system's response under such perturbation and improves the quality of system response by removing fragilities and introducing adaptive fault tolerance strategies.</p></div><div><h3>Method</h3><p>The UNFRAGILE framework's feasibility has been validated by applying it to a cloud-native using a real-world architecture to enhance its antifragility towards long outbound service latencies. The empirical investigation of fragility is undertaken, and the results show how chaos affects application performance metrics and causes disturbances in them. To deal with chaotic network latency, an adaptation phase is put into effect.</p></div><div><h3>Results</h3><p>The findings indicate that the steady stage's behaviour is like the antifragile stage's behaviour. This suggests that the system could self-stabilise during the chaos without the need to define a static configuration after determining from the context of the environment that the dependent system was experiencing difficulties.</p></div><div><h3>Conclusion</h3><p>Overall, this paper contributes to ongoing efforts to develop antifragile software capable of adapting to the rapidly changing complex environment. Overall, the research provides an operational framework for engineering software systems that learn and improve through exposure to failures rather than just surviving them.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107519"},"PeriodicalIF":3.8,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001241/pdfft?md5=4a3b226c5cce52766d27e46c6f346db1&pid=1-s2.0-S0950584924001241-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483209","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":"Measuring and improving software testability at the design level","authors":"Morteza Zakeri-Nasrabadi ,&nbsp;Saeed Parsa ,&nbsp;Sadegh Jafari","doi":"10.1016/j.infsof.2024.107511","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107511","url":null,"abstract":"<div><h3>Context</h3><p>The quality of software systems is significantly influenced by design testability, an aspect often overlooked during the initial phases of software development. The implementation may deviate from its design, resulting in decreased testability at the integration and unit levels.</p></div><div><h3>Objective</h3><p>The objective of this study is to automatically identify low-testable parts in object-orientated design and enhance them by refactoring to design patterns. The impact of various design metrics mainly coupling (<em>e.g.</em>, fan-in and fan-out) and inheritance (<em>e.g.</em>, depth of inheritance tree and number of subclasses) metrics on design testability is measured to select the most appropriate refactoring candidates.</p></div><div><h3>Method</h3><p>The methodology involves creating a machine learning model for design testability prediction using a large dataset of Java classes, followed by developing an automated refactoring tool. The design classes are vectorized by ten design metrics and labeled with testability scores calculated from a mathematical model. The model computes testability based on code coverage and test suite size of classes that have already been tested via automatic tools. A voting regressor model is trained to predict the design testability of any class diagram based on these design metrics. The proposed refactoring tool for dependency injection and factory method is applied to various open-source Java projects, and its impact on design testability is assessed.</p></div><div><h3>Results</h3><p>The proposed design testability model demonstrates its effectiveness by satisfactorily predicting design testability, as indicated by a mean squared error of 0.04 and an R² score of 0.53. The automated refactoring tool has been successfully evaluated on six open-source Java projects, revealing an enhancement in design testability by up to 19.11 %.</p></div><div><h3>Conclusion</h3><p>The proposed automated approach offers software developers the means to continuously evaluate and enhance design testability throughout the entire software development life cycle, mitigating the risk of testability issues stemming from design-to-implementation discrepancies.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107511"},"PeriodicalIF":3.8,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438939","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":"SeDPGK: Semi-supervised software defect prediction with graph representation learning and knowledge distillation","authors":"Wangshu Liu ,&nbsp;Ye Yue ,&nbsp;Xiang Chen ,&nbsp;Qing Gu ,&nbsp;Pengzhan Zhao ,&nbsp;Xuejun Liu ,&nbsp;Jianjun Zhao","doi":"10.1016/j.infsof.2024.107510","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107510","url":null,"abstract":"<div><h3>Context:</h3><p>Constructing an effective defect prediction model relies on a substantial number of labeled program modules. Unfortunately, program module labeling is often time-consuming and error-prone. Semi-supervised software defect prediction (SSDP) can alleviate this issue by incorporating some labeled modules and the remaining unlabeled modules from the same project.</p></div><div><h3>Objective:</h3><p>However, previous SSDP methods ignore the significant influence of dependencies between software modules. The potential of knowledge distillation in leveraging labeled instances to guide the learning process and effectively utilizing information from unlabeled instances to improve SSDP performance has not been fully investigated.</p></div><div><h3>Method:</h3><p>We propose a novel approach SeDPGK. Specifically, to exploit the graph-structured knowledge, we first construct the program dependence graph to extract control and data dependencies among modules. Then we use graph neural networks (GNNs) to learn the graph representation of the module relationships and encode with the statement semantics of abstract syntax tree and traditional static features for diversity. Second, we integrate multiple GNNs jointly trained as teacher models to ensemble various styles of graph-based networks and generate trustworthy labels for unlabeled modules. Further, to preserve the teacher model’s sufficient structure and semantic knowledge, we adopt a trainable label propagation and multi-layer perception as the student model and mitigate the differences between the teacher and student models using two widespread knowledge distillation functions.</p></div><div><h3>Results:</h3><p>We conducted our experiments on 17 real-world projects. The experimental results show that SeDPGK outperforms semi-supervised baselines with an average improvement of 16.9% for PD, 42.5% for FAR, and 8.9% for AUC, respectively. Moreover, the performance improvement is consistently significant across multiple statistical tests.</p></div><div><h3>Conclusion:</h3><p>The effectiveness of SeDPGK comes from the aggregation of the different GNNs with heterogeneity. Moreover, the graph structure and semantic features hidden behind the source code play a crucial role in the distillation framework.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107510"},"PeriodicalIF":3.8,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483210","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 vulnerability detection framework by focusing on critical execution paths","authors":"Jianxin Cheng ,&nbsp;Yizhou Chen ,&nbsp;Yongzhi Cao ,&nbsp;Hanpin Wang","doi":"10.1016/j.infsof.2024.107517","DOIUrl":"10.1016/j.infsof.2024.107517","url":null,"abstract":"<div><h3>Context:</h3><p>Vulnerability detection is critical to ensure software security, and detecting vulnerabilities in smart contract code is currently gaining massive attention. Existing deep learning-based vulnerability detection methods represent the code as a code structure graph and eliminate vulnerability-irrelevant nodes. Then, they learn vulnerability-related code features from the simplified graph for vulnerability detection. However, this simplified graph struggles to represent relatively complete structural information of code, which may affect the performance of existing vulnerability detection methods.</p></div><div><h3>Objective:</h3><p>In this paper, we present a novel <strong>V</strong>ulnerability <strong>D</strong>etection framework based on <strong>C</strong>ritical <strong>E</strong>xecution <strong>P</strong>aths (VDCEP), which aims to improve smart contract vulnerability detection.</p></div><div><h3>Method:</h3><p>Firstly, given a code structure graph, we deconstruct it into multiple execution paths that reflect rich structural information of code. To reduce irrelevant code information, a path selection strategy is employed to identify critical execution paths that may contain vulnerable code information. Secondly, a feature extraction module is adopted to learn feature representations of critical paths. Finally, we feed all path feature representations into a classifier for vulnerability detection. Also, the feature weights of paths are provided to measure their importance in vulnerability detection.</p></div><div><h3>Results:</h3><p>We evaluate VDCEP on a large dataset with four types of smart contract vulnerabilities. Results show that VDCEP outperforms 14 representative vulnerability detection methods by 5.34%–60.88% in F1-score. The ablation studies analyze the effects of our path selection strategy and feature extraction module on VDCEP. Moreover, VDCEP still outperforms ChatGPT by 34.46% in F1-score.</p></div><div><h3>Conclusion:</h3><p>Compared to existing vulnerability detection methods, VDCEP is more effective in detecting smart contract vulnerabilities by utilizing critical execution paths. Besides, we can provide interpretable details about vulnerability detection by analyzing the path feature weights.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107517"},"PeriodicalIF":3.8,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141396583","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":"Product managers in software startups: A grounded theory","authors":"Jorge Melegati ,&nbsp;Igor Wiese ,&nbsp;Eduardo Guerra ,&nbsp;Rafael Chanin ,&nbsp;Abdullah Aldaeej ,&nbsp;Tommi Mikkonen ,&nbsp;Rafael Prikladnicki ,&nbsp;Xiaofeng Wang","doi":"10.1016/j.infsof.2024.107516","DOIUrl":"10.1016/j.infsof.2024.107516","url":null,"abstract":"<div><h3>Context:</h3><p>Defining and designing a software product is not merely a technical endeavor, but also a socio-technical journey. As such, its success is associated with human-related aspects, such as the value users perceive. To handle this issue, the product manager role has become more evident in software-intensive companies. A unique, challenging context for these professionals is constituted by software startups, emerging companies developing novel solutions looking for sustainable and scalable business models.</p></div><div><h3>Objective:</h3><p>This study aims to describe the role of product managers in the context of software startups.</p></div><div><h3>Method:</h3><p>We performed a Socio-Technical Grounded Theory study using data from blog posts and interviews.</p></div><div><h3>Results:</h3><p>The results describe the product manager as a multidisciplinary, general role, not only guiding the product by developing its vision but also as a connector that emerges in a growing company, enabling communication of software development with other areas, mainly business and user experience. The professional performing this role has a background in one of these areas but a broad knowledge and understanding of key concepts of the other areas is needed. We also describe how differences of this role to other lead roles are perceived in practice.</p></div><div><h3>Conclusions:</h3><p>Our findings represent several implications for research, such as better understanding of the role transformation in growing software startups, practice, e.g., identifying the points to which a professional migrating to this role should pay attention, and the education of future software developers, by suggesting the inclusion of related topics in the education and training of future software engineers.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107516"},"PeriodicalIF":3.9,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001216/pdfft?md5=bdf7a653e0eee5cde2e2aff9308d4ff0&pid=1-s2.0-S0950584924001216-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141396895","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}