Journal of Systems and Software最新文献

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Extracting microservices from monolithic applications using consistent graph enhanced Graph Transformer

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-17 DOI: 10.1016/j.jss.2025.112345

Xueyin Wei , Jing Li , Xudong He , Weizhou Peng , Ying Zhu , Rongbin Gu , Yunlong Zhu , Jun Huang

With the continuous development of cloud computing, the advantages of microservice architecture have become increasingly obvious compared with monolithic programs. Consequently, numerous firms are engaged in research aimed at transitioning legacy monolithic applications to microservices, thereby enabling them to maximize the advantages of cloud-based deployment. To solve the problem that manual decomposition of microservices is both time-consuming and labor-intensive, while traditional automated microservice decomposition methods cannot effectively integrate the rich structural and semantic information of single programs, we propose a microservice extraction method based on consistent graph clustering (GC-VCG). Initially, a static analysis strategy is employed to extract dependencies between classes within the monolithic application, as well as the textual information utilized in the class creation process, to construct both the structural and semantic views. Subsequently, a consistent graph enhanced Graph Transformer is utilized to learn a unified graph from both structural and semantic views. Lastly, the k-means clustering algorithm is applied to cluster the nodes, thereby identifying candidate microservices. To verify the effectiveness of GC-VCG, this paper compares it with multiple baseline methods on four publicly available monolithic applications. The results show the effectiveness of GC-VCG.

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引用次数: 0

On the understandability of machine learning practices in deep learning and reinforcement learning based systems

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-16 DOI: 10.1016/j.jss.2025.112343

Evangelos Ntentos , Stephen John Warnett , Uwe Zdun

Machine learning (ML) has emerged as a transformative subject, using various algorithms to help systems analyze data and make predictions. Deep Learning (DL) uses neural networks to address hard problems. Reinforcement Learning (RL) is a way to solve problems by making consecutive decisions.

Understanding ML systems based only on the source code is often a challenging task, especially for inexperienced developers. In a controlled experiment involving one hundred fifty-eight participants, we assessed the understandability of ML-based systems and workflows through source code inspection compared to semi-formal representations in models and metrics.

We hypothesize that ML system diagrams modeling details of ML workflows and practices like transfer learning and checkpoints can enhance the understandability of ML practices in system design comprehension tasks, assessed through task correctness. Additionally, providing these sources could lead to an increase in task duration, and we expect a significant correlation between correctness and duration.

Our findings show that providing semi-formal ML system diagrams with the source code improves the effectiveness of the correctness for the DL relevant tasks. The control group had an average correctness of 0.7121, while the experimental group had a higher average correctness of 0.7759. On the other hand, participants who received only the system source code showed slightly better performance in the correctness task (average correctness 0.6808) within the RL relevant tasks compared to those who also received the semi-formal diagrams (average correctness of 0.6612). However, no significant difference was found in the duration task between the two. The control group, for the DL relevant tasks, took an average of 1571.62 s, whereas the experimental group took an average of 1763.85 s. For the RL relevant tasks, the control group had an average of 1883.80 s, while the experimental group 1925.46 s. However, semi-formal ML system diagrams can benefit specific scenarios.

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引用次数: 0

CIPAC: A framework of automated software construction based on collective intelligence

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-16 DOI: 10.1016/j.jss.2025.112335

Jiaxin Liu, Yating Zhang, Yiwei Li, Tiecheng Ma, Wei Dong

In software development, constructing programs efficiently and accurately is essential, which leads to the rise of program synthesis. However, the complexity of program spaces and the diversity of user intents restrict the scale and quality of the code generated, resulting in many works focused on generating function-level code. To address the automated generation of complex code, we propose an automated program construction process model CIPAC based on collective intelligence, which can generate software-level code automatically. CIPAC incorporates methods for the automated aggregation of collective intelligence, the construction of software structures and task specifications, efficient program generation and search, and the optimization of code quality and composition. CIPAC employs explainable program synthesis methods as its core to ensure reliability and leverages collective intelligence throughout the entire software development lifecycle. To validate the effectiveness of CIPAC, we conduct a case study for developing a matrix operation application and explore complex tasks in the aerospace domain. The results show that our platform enables the construction of the software project compared to existing methods. To demonstrate reliability, we conduct integration testing, system testing, and security validation, with results indicating that the generated project passes all tests without security issues.

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引用次数: 0

Best practices for evaluating IRFL approaches

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-16 DOI: 10.1016/j.jss.2025.112342

Thomas Hirsch, Birgit Hofer

Information retrieval fault localization (IRFL) is a popular research field and many IRFL approaches have been proposed recently. Unfortunately, the evaluation of some of these IRFL approaches is often too simplistic, which can cause an overestimation of performance of these approaches. In this paper, we discuss evaluation pitfalls and problems. Furthermore, we propose best practices to avoid them. In detail, we discuss evaluation strategies such as parameter tuning and temporal dependencies in the data, dataset issues, metrics, statistical significance testing, and the unavailability of supplemental material. To support our claim of the poor status quo of current evaluation practices in some research papers, we have performed a literature survey on 135 papers. We hope that this paper will help researchers to avoid the described pitfalls in their evaluation of IRFL approaches.

引用次数: 0

Job satisfaction at risk: Measuring the role of process debt in agile software development

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-16 DOI: 10.1016/j.jss.2025.112350

Tomas Gustavsson , Muhammad Ovais Ahmad , Hina Saeeda

Process debt (PD) in agile software development represents inefficiencies that undermine team performance and job satisfaction. This study investigates the quantitative impact of PD on job satisfaction within agile teams, surveying 191 participants from two software development organizations. Our research examines five PD types: Process Unsuitability, Roles Debt, Synchronization Debt, Documentation Debt, and Infrastructure Debt. Using multiple regression analysis, our model explains approximately 33.8 % of the variance in job satisfaction. Among the five PD types, Process Unsuitability and Roles Debt emerged as statistically significant predictors of reduced job satisfaction. These findings indicate that certain forms of PD have a measurable negative impact on developers’ perceptions of their work environment. By identifying which PD types most strongly influence job satisfaction, this research offers empirically grounded insights that can inform targeted interventions. Understanding and addressing the most impactful PD categories may help organizations refine agile processes, thereby mitigating the detrimental effects of process inefficiencies on job satisfaction.

{"title":"Job satisfaction at risk: Measuring the role of process debt in agile software development","authors":"Tomas Gustavsson , Muhammad Ovais Ahmad , Hina Saeeda","doi":"10.1016/j.jss.2025.112350","DOIUrl":"10.1016/j.jss.2025.112350","url":null,"abstract":"<div><div>Process debt (PD) in agile software development represents inefficiencies that undermine team performance and job satisfaction. This study investigates the quantitative impact of PD on job satisfaction within agile teams, surveying 191 participants from two software development organizations. Our research examines five PD types: Process Unsuitability, Roles Debt, Synchronization Debt, Documentation Debt, and Infrastructure Debt. Using multiple regression analysis, our model explains approximately 33.8 % of the variance in job satisfaction. Among the five PD types, Process Unsuitability and Roles Debt emerged as statistically significant predictors of reduced job satisfaction. These findings indicate that certain forms of PD have a measurable negative impact on developers’ perceptions of their work environment. By identifying which PD types most strongly influence job satisfaction, this research offers empirically grounded insights that can inform targeted interventions. Understanding and addressing the most impactful PD categories may help organizations refine agile processes, thereby mitigating the detrimental effects of process inefficiencies on job satisfaction.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"222 ","pages":"Article 112350"},"PeriodicalIF":3.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104064","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}

引用次数: 0

Understanding the low inter-rater agreement on aggressiveness on the Linux Kernel Mailing List

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-15 DOI: 10.1016/j.jss.2025.112339

Thomas Bock , Niklas Schneider , Angelika Schmid , Sven Apel , Janet Siegmund

Communication among software developers plays an essential role in open-source software (OSS) projects. Not unexpectedly, previous studies have shown that the conversational tone and, in particular, aggressiveness influence the participation of developers in OSS projects. Therefore, we aimed at studying aggressive communication behavior on the Linux Kernel Mailing List (LKML), which is known for aggressive e-mails of some of its contributors. To that aim, we attempted to assess the extent of aggressiveness of 720 e-mails from the LKML with a human annotation study, involving multiple annotators, to select a suitable sentiment analysis tool.

The results of our annotation study revealed that there is substantial disagreement, even among humans, which uncovers a deeper methodological challenge of studying aggressiveness in the software-engineering domain. Adjusting our focus, we dug deeper and investigated why the agreement among humans is generally low, based on manual investigations of ambiguously rated e-mails. Our results illustrate that human perception is individual and context dependent, especially when it comes to technical content. Thus, when identifying aggressiveness in software-engineering texts, it is not sufficient to rely on aggregated measures of human annotations. Hence, sentiment analysis tools specifically trained on human-annotated data do not necessarily match human perception of aggressiveness, and corresponding results need to be taken with a grain of salt. By reporting our results and experience, we aim at confirming and raising additional awareness of this methodological challenge when studying aggressiveness (and sentiment, in general) in the software-engineering domain.

{"title":"Understanding the low inter-rater agreement on aggressiveness on the Linux Kernel Mailing List","authors":"Thomas Bock , Niklas Schneider , Angelika Schmid , Sven Apel , Janet Siegmund","doi":"10.1016/j.jss.2025.112339","DOIUrl":"10.1016/j.jss.2025.112339","url":null,"abstract":"<div><div>Communication among software developers plays an essential role in open-source software (OSS) projects. Not unexpectedly, previous studies have shown that the conversational tone and, in particular, aggressiveness influence the participation of developers in OSS projects. Therefore, we aimed at studying aggressive communication behavior on the Linux Kernel Mailing List (LKML), which is known for aggressive e-mails of some of its contributors. To that aim, we attempted to assess the extent of aggressiveness of 720 e-mails from the LKML with a human annotation study, involving multiple annotators, to select a suitable sentiment analysis tool.</div><div>The results of our annotation study revealed that there is substantial disagreement, even among humans, which uncovers a deeper methodological challenge of studying aggressiveness in the software-engineering domain. Adjusting our focus, we dug deeper and investigated why the agreement among humans is generally low, based on manual investigations of ambiguously rated e-mails. Our results illustrate that human perception is individual and context dependent, especially when it comes to technical content. Thus, when identifying aggressiveness in software-engineering texts, it is not sufficient to rely on aggregated measures of human annotations. Hence, sentiment analysis tools specifically trained on human-annotated data do not necessarily match human perception of aggressiveness, and corresponding results need to be taken with a grain of salt. By reporting our results and experience, we aim at confirming and raising additional awareness of this methodological challenge when studying aggressiveness (and sentiment, in general) in the software-engineering domain.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"222 ","pages":"Article 112339"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104111","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}

引用次数: 0

Engineering patterns for Trust and Safety on social media platforms: A case study of Mastodon and Diaspora

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-15 DOI: 10.1016/j.jss.2024.112322

Geoffrey Cramer, William P. Maxam III, James C. Davis

Context:

Trust & Safety (T&S) Engineering is an emerging area of software engineering that mitigates the risks of harmful interactions in online platforms. Numerous studies have explored T&S risks on social media platforms, taxonomizing threats and investigating individual issues. However, there is limited empirical knowledge about engineering efforts to promote T&S.

Methods:

This study examines T&S risks and the engineering patterns to resolve them. We conducted a case study of the two largest decentralized SMPs: Mastodon and Diaspora. These SMPs are open-source, so we analyzed T&S discussions within 60 GitHub issues. We analyzed T&S discussions that took place in their online repository and extracted T&S risks, T&S engineering patterns, and resolution rationales considered by the engineers. We integrate our findings by mapping T&S engineering patterns onto a general model of SMPs, to give SMP engineers a systematic understanding of their T&S risk treatment options.

Results:

T&S issues are a challenge throughout the feature set and lifespan of an SMP. A taxonomy of 12 solution patterns are developed, paving the way for academia and industry to standardize Trust & Safety solutions. We conclude with future directions to study and improve T&S Engineering, spanning software design, decision-making, and validation. We conclude with future directions to study and improve T&S Engineering, spanning software design, decision-making, and validation.

{"title":"Engineering patterns for Trust and Safety on social media platforms: A case study of Mastodon and Diaspora","authors":"Geoffrey Cramer, William P. Maxam III, James C. Davis","doi":"10.1016/j.jss.2024.112322","DOIUrl":"10.1016/j.jss.2024.112322","url":null,"abstract":"<div><h3>Context:</h3><div>Trust & Safety (T&S) Engineering is an emerging area of software engineering that mitigates the risks of harmful interactions in online platforms. Numerous studies have explored T&S risks on social media platforms, taxonomizing threats and investigating individual issues. However, there is limited empirical knowledge about engineering efforts to promote T&S.</div></div><div><h3>Methods:</h3><div>This study examines T&S risks and the engineering patterns to resolve them. We conducted a case study of the two largest decentralized SMPs: Mastodon and Diaspora. These SMPs are open-source, so we analyzed T&S discussions within 60 GitHub issues. We analyzed T&S discussions that took place in their online repository and extracted T&S risks, T&S engineering patterns, and resolution rationales considered by the engineers. We integrate our findings by mapping T&S engineering patterns onto a general model of SMPs, to give SMP engineers a systematic understanding of their T&S risk treatment options.</div></div><div><h3>Results:</h3><div>T&S issues are a challenge throughout the feature set and lifespan of an SMP. A taxonomy of 12 solution patterns are developed, paving the way for academia and industry to standardize Trust & Safety solutions. We conclude with future directions to study and improve T&S Engineering, spanning software design, decision-making, and validation. We conclude with future directions to study and improve T&S Engineering, spanning software design, decision-making, and validation.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"222 ","pages":"Article 112322"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104114","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}

引用次数: 0

Runtime defect prediction of industrial business processes: A focused look at real-life SAP systems

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-15 DOI: 10.1016/j.jss.2024.112306

Max Nijholt , Giovanni Quattrocchi , Damian Andrew Tamburri

Business process operations are the dominant logic underpinning most of the service-based applications currently in use. Situated in the field of SAP business processes — commonly referred to as iFlows — and their integration, this paper looks into the defectiveness of such flows with a Machine-Learning approach. We propose to cluster and classify at runtime the Integration Flows of business processes during their orchestration; we do so by using metrics extracted from the Integration of 400+ complex business interaction and service orchestration Flows along with their metadata. Through a combined ensemble-based, clustering, and supervised learning exercise, we conclude that an AI-based approach for runtime defect prediction of iFlows shows considerable promise in providing actionable insights for better orchestration intelligence, especially in sight of self-aware business processes of the future.

引用次数: 0

Navigating the DevOps landscape

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-14 DOI: 10.1016/j.jss.2024.112331

Xinrui Zhang , Pincan Zhao , Jason Jaskolka

Context:

DevOps, with its increasing prevalence in both industry and academia, has evolved into various DevOps variants (namely XOps) to address emerging technological and operational challenges. However, this proliferation has created confusion and a lack of clarity about the systematic understanding of these XOps and their interrelationship in the DevOps landscape, leading to fragmented knowledge and application.

Objective:

This research seeks to construct a comprehensive picture of the existing DevOps landscape, clarifying the nature and nuances of various XOps, to guide effective future studies and implementations.

Method:

Utilizing Multivocal Literature Review (MLR), 80 gathered documents are thoroughly examined from throughout the whole community, encompassing both white and grey literature, to map the DevOps landscape.

Results:

Our review systematically discovered 38 XOps terms and 13 well-studied XOps including AIOps, BizDevOps, CloudOps, DataOps, DevSecOps, FinOps, GitOps, MLOps, ModelOps, NetDevOps, NoOps, SecDevOps and TwinOps. We provided dictionary-like resource that elucidates the core concepts and main ideas associated with each XOps. An in-depth understanding of intricate evolution from DevOps to XOps is delved into, supplemented by the research of relationships between XOps and various technological enablers as well as relationships between XOps and organizational teams, contributing to the ongoing dialogue surrounding their application and evolution.

Implications:

This paper provides a foundational understanding of the DevOps landscape including open issues and challenges, current and future trends, assisting both researchers and practitioners in navigating this complex field. It establishes a platform for further research and practical applications in the evolving field of DevOps and XOps.

{"title":"Navigating the DevOps landscape","authors":"Xinrui Zhang , Pincan Zhao , Jason Jaskolka","doi":"10.1016/j.jss.2024.112331","DOIUrl":"10.1016/j.jss.2024.112331","url":null,"abstract":"<div><h3>Context:</h3><div>DevOps, with its increasing prevalence in both industry and academia, has evolved into various DevOps variants (namely XOps) to address emerging technological and operational challenges. However, this proliferation has created confusion and a lack of clarity about the systematic understanding of these XOps and their interrelationship in the DevOps landscape, leading to fragmented knowledge and application.</div></div><div><h3>Objective:</h3><div>This research seeks to construct a comprehensive picture of the existing DevOps landscape, clarifying the nature and nuances of various XOps, to guide effective future studies and implementations.</div></div><div><h3>Method:</h3><div>Utilizing Multivocal Literature Review (MLR), 80 gathered documents are thoroughly examined from throughout the whole community, encompassing both white and grey literature, to map the DevOps landscape.</div></div><div><h3>Results:</h3><div>Our review systematically discovered 38 XOps terms and 13 well-studied XOps including AIOps, BizDevOps, CloudOps, DataOps, DevSecOps, FinOps, GitOps, MLOps, ModelOps, NetDevOps, NoOps, SecDevOps and TwinOps. We provided dictionary-like resource that elucidates the core concepts and main ideas associated with each XOps. An in-depth understanding of intricate evolution from DevOps to XOps is delved into, supplemented by the research of relationships between XOps and various technological enablers as well as relationships between XOps and organizational teams, contributing to the ongoing dialogue surrounding their application and evolution.</div></div><div><h3>Implications:</h3><div>This paper provides a foundational understanding of the DevOps landscape including open issues and challenges, current and future trends, assisting both researchers and practitioners in navigating this complex field. It establishes a platform for further research and practical applications in the evolving field of DevOps and XOps.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"223 ","pages":"Article 112331"},"PeriodicalIF":3.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386682","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}

引用次数: 0

Characterizing and detecting Python version incompatibilities caused by inconsistent version specifications

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Journal of Systems and Software

Pub Date : 2025-01-14 DOI: 10.1016/j.jss.2025.112337

Shuo Li , Haocheng Gao , Wei Chen , Yi Li , Haoxiang Tian , Chengwei Liu , Dan Ye

Python interpreter serves as the foundation for developing Python projects. Configuring compatible Python version is essential for packaging and reusing third-party libraries (TPLs) in the Python ecosystem. When the Python distribution version range constraint of a third-party library is incorrectly declared, it may lead to installation failures or runtime errors when the library is used by a client project. We call such errors Python Distribution Version Specification Error (PDSpecErr). PDSpecErrs are not studied adequately and there still lacks a method to guarantee the correctness of Python version specifications. To combat PDSpecErrs and mitigate their adverse impacts on downstream applications, we conducted the first exploratory study on 3000 real-world TPLs to investigate PDSpecErrs about their prevalence, symptom categories, and diagnostic patterns. Based on the comprehensive study, we designed and implemented PyChecker, a tool to automatically detect PDSpecErrs in TPLs, locate root causes, and generate fixing solutions. We evaluated PyChecker on 3000 TPL releases and PyChecker detected 842 PDSpecErrs, reporting their root causes and recommending fixing solutions. We have reported 52 PDSpecErrs to the concerned developers. So far, 32 issues have been confirmed and fixed according to our suggestions.

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