Pub Date : 2026-02-01Epub Date: 2025-11-11DOI: 10.1016/j.infsof.2025.107970
Juho-Pekka Mäkipää , Junichi Iijima
As more and more services are becoming available only in digital form, self-service technologies (SSTs) need to be accessible to ensure all citizens have equal opportunities to participate in society. However, SSTs’ accessibility is still insufficient, and the overall picture of possible accessibility issues with SSTs is fragmented. In this study, we evaluated the accessibility of a sample of 20 SSTs in Japan by examining variables in user perception and action, factors related to cognitive accessibility, and user interface components. The findings are twofold. First, we illustrated the multimodalities in SST interaction based on the theory of human-computer interaction. Then, we identified SST user interface design practices that impact human cognition. This study illustrates the current reality of how accessibility is actualized and proposes future research directions and practices for the SST industry to develop and improve SSTs’ accessibility.
{"title":"Accessibility evaluation of interaction modalities and cognitive process of self-service technologies’ user interface in Japan","authors":"Juho-Pekka Mäkipää , Junichi Iijima","doi":"10.1016/j.infsof.2025.107970","DOIUrl":"10.1016/j.infsof.2025.107970","url":null,"abstract":"<div><div>As more and more services are becoming available only in digital form, self-service technologies (SSTs) need to be accessible to ensure all citizens have equal opportunities to participate in society. However, SSTs’ accessibility is still insufficient, and the overall picture of possible accessibility issues with SSTs is fragmented. In this study, we evaluated the accessibility of a sample of 20 SSTs in Japan by examining variables in user perception and action, factors related to cognitive accessibility, and user interface components. The findings are twofold. First, we illustrated the multimodalities in SST interaction based on the theory of human-computer interaction. Then, we identified SST user interface design practices that impact human cognition. This study illustrates the current reality of how accessibility is actualized and proposes future research directions and practices for the SST industry to develop and improve SSTs’ accessibility.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107970"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571428","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}
Pub Date : 2026-02-01Epub Date: 2025-10-25DOI: 10.1016/j.infsof.2025.107939
Ahsen Maqsoom , Ifra Zahoor , Muhammad Umer , Hassan Ashraf , Lapyote Prasittisopin , Usama Arif
Context
Innovative thinking and effective leadership are critical, but Pakistan’s software sector lags behind global tech firms, indicating a leadership and innovation gap.
Objectives
This study examines how three dimensions of paternalistic leadership authoritarian, benevolent, and moral affect employee creativity and whether leader–member exchange (LMX) mediates and psychological capital moderates these effects.
Methods
Data were collected via a 2021 online survey of 216 software employees in Punjab and Khyber Pakhtunkhwa (KPK) and analyzed using PLS-SEM.
Results
Moral and benevolent leadership significantly enhance employee creativity, whereas authoritarian leadership has no significant effect. LMX fully mediates all three styles’ effects on creativity, but psychological capital does not significantly moderate the link between paternalistic leadership and LMX.
Conclusion
These findings emphasize the crucial role of moral and benevolent leadership in fostering employee creativity through strong leader–member relationships. This study contributes to leadership literature by offering evidence from an underexplored context and provides actionable guidance for enhancing innovation in Pakistan’s software sector.
{"title":"Impact of paternalistic leadership on employee creativity: Exploring the role of leader-member-exchange and psychological capital in the software industry","authors":"Ahsen Maqsoom , Ifra Zahoor , Muhammad Umer , Hassan Ashraf , Lapyote Prasittisopin , Usama Arif","doi":"10.1016/j.infsof.2025.107939","DOIUrl":"10.1016/j.infsof.2025.107939","url":null,"abstract":"<div><h3>Context</h3><div>Innovative thinking and effective leadership are critical, but Pakistan’s software sector lags behind global tech firms, indicating a leadership and innovation gap.</div></div><div><h3>Objectives</h3><div>This study examines how three dimensions of paternalistic leadership authoritarian, benevolent, and moral affect employee creativity and whether leader–member exchange (LMX) mediates and psychological capital moderates these effects.</div></div><div><h3>Methods</h3><div>Data were collected via a 2021 online survey of 216 software employees in Punjab and Khyber Pakhtunkhwa (KPK) and analyzed using PLS-SEM.</div></div><div><h3>Results</h3><div>Moral and benevolent leadership significantly enhance employee creativity, whereas authoritarian leadership has no significant effect. LMX fully mediates all three styles’ effects on creativity, but psychological capital does not significantly moderate the link between paternalistic leadership and LMX.</div></div><div><h3>Conclusion</h3><div>These findings emphasize the crucial role of moral and benevolent leadership in fostering employee creativity through strong leader–member relationships. This study contributes to leadership literature by offering evidence from an underexplored context and provides actionable guidance for enhancing innovation in Pakistan’s software sector.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107939"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420525","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}
Pub Date : 2026-02-01Epub Date: 2025-11-19DOI: 10.1016/j.infsof.2025.107974
José L. Risco-Martín , Román Cárdenas , Segundo Esteban , Patricia Arroba
Context:
Deploying complex Cyber-Physical Systems (CPSs) is challenging due to the gap between abstract design models and their physical implementation. This often requires manual recoding, an error-prone process that breaks the continuity from a verified model to the final deployed system.
Objective:
To bridge this gap, this paper introduces a methodology that enables a direct and seamless transition from a formal computational model to its physical deployment, eliminating the need for manual recoding. The core aim is to use a single, unmodified model for both simulation and real-world operation.
Methods:
We propose a Model-Based Systems Engineering (MBSE) methodology grounded in the Discrete Event System Specification (DEVS) formalism. Its key innovation is the formalization of the Digital Twin (DT) concept as a reusable, executable DEVS coupled model, which explicitly structures the interface between the system’s digital logic and its physical counterpart. The methodology is implemented using the xDEVS simulation engine, whose Real-Time (RT) capabilities and built-in hardware protocol handlers (e.g., Inter-Integrated Circuit (I2C), MQTT) allow the formal model to directly control physical components.
Results:
We demonstrated the methodology by adapting the purely computational DEVS-BLOOM model to a physical emulation controlling a small-scale Unmanned Surface Vehicle (USV). Field tests confirmed the physical USV, operated by the unmodified DEVS model running in real-time, successfully performed its autonomous navigation and monitoring mission. This successful validation is demonstrated using this single-case study as a foundational proof-of-concept.
Conclusion:
Our approach provides a robust and seamless pathway from a verified computational model to a reliable real-world system. With the formalization of the physical–digital interface inside the model itself, the methodology effectively closes the abstraction–implementation gap in CPS development.
{"title":"A DEVS-based MBSE methodology for seamless deployment via Formal Digital Twin architectures","authors":"José L. Risco-Martín , Román Cárdenas , Segundo Esteban , Patricia Arroba","doi":"10.1016/j.infsof.2025.107974","DOIUrl":"10.1016/j.infsof.2025.107974","url":null,"abstract":"<div><h3>Context:</h3><div>Deploying complex Cyber-Physical Systems (CPSs) is challenging due to the gap between abstract design models and their physical implementation. This often requires manual recoding, an error-prone process that breaks the continuity from a verified model to the final deployed system.</div></div><div><h3>Objective:</h3><div>To bridge this gap, this paper introduces a methodology that enables a direct and seamless transition from a formal computational model to its physical deployment, eliminating the need for manual recoding. The core aim is to use a single, unmodified model for both simulation and real-world operation.</div></div><div><h3>Methods:</h3><div>We propose a Model-Based Systems Engineering (MBSE) methodology grounded in the Discrete Event System Specification (DEVS) formalism. Its key innovation is the formalization of the Digital Twin (DT) concept as a reusable, executable DEVS coupled model, which explicitly structures the interface between the system’s digital logic and its physical counterpart. The methodology is implemented using the xDEVS simulation engine, whose Real-Time (RT) capabilities and built-in hardware protocol handlers (e.g., Inter-Integrated Circuit (I<sup>2</sup>C), MQTT) allow the formal model to directly control physical components.</div></div><div><h3>Results:</h3><div>We demonstrated the methodology by adapting the purely computational DEVS-BLOOM model to a physical emulation controlling a small-scale Unmanned Surface Vehicle (USV). Field tests confirmed the physical USV, operated by the unmodified DEVS model running in real-time, successfully performed its autonomous navigation and monitoring mission. This successful validation is demonstrated using this single-case study as a foundational proof-of-concept.</div></div><div><h3>Conclusion:</h3><div>Our approach provides a robust and seamless pathway from a verified computational model to a reliable real-world system. With the formalization of the physical–digital interface inside the model itself, the methodology effectively closes the abstraction–implementation gap in CPS development.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107974"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571426","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}
Pub Date : 2026-02-01Epub Date: 2025-10-31DOI: 10.1016/j.infsof.2025.107956
Giordano d’Aloisio , Tosin Fadahunsi , Antinisca Di Marco , Federica Sarro
Context:
Generative models are nowadays widely used to generate graphical content used for multiple purposes. However, it has been shown that the images generated by these models could reinforce societal biases already existing in specific contexts. The Software Engineering (SE) community is not immune to gender and ethnicity disparities, which could be amplified by the use of these models. Hence, if used without consciousness, artificially generated images could reinforce these biases in the SE domain.
Objective:
In this paper, we focus on understanding the implicit bias exposed by general-purpose open-source image generation models towards SE tasks. In addition, we investigate the extent to which it is possible to mitigate the bias by using prompt engineering techniques.
Methods:
We perform an extensive empirical evaluation of the implicit gender and ethnicity bias exposed by six popular open-source image generation models towards SE tasks. We obtain 20,160 images by feeding each model with three sets of prompts describing different software-related tasks: One set does not include any specification of the person performing the task, one set specifies that the person performing the task is a Software Engineer, and the last set explicitly request a fair representation of different genders and ethnicities. Next, we evaluate the gender and ethnicity disparities in the generated images.
Results:
The results indicate that all models exhibit a significant bias related to gender and ethnicity in SE tasks. Furthermore, we demonstrate that prompt engineering effectively reduces gender bias in only one of the six models; however, none of the models achieves fair representation with respect to ethnicity.
Conclusion:
The results of our analysis highlight serious concerns about the adoption of these models to generate content for SE tasks and open the field for future research on bias mitigation in this context.
{"title":"How do generative models draw a software engineer? An empirical study on implicit bias of open-source image generation models","authors":"Giordano d’Aloisio , Tosin Fadahunsi , Antinisca Di Marco , Federica Sarro","doi":"10.1016/j.infsof.2025.107956","DOIUrl":"10.1016/j.infsof.2025.107956","url":null,"abstract":"<div><h3>Context:</h3><div>Generative models are nowadays widely used to generate graphical content used for multiple purposes. However, it has been shown that the images generated by these models could reinforce societal biases already existing in specific contexts. The Software Engineering (SE) community is not immune to <em>gender</em> and <em>ethnicity</em> disparities, which could be amplified by the use of these models. Hence, if used without consciousness, artificially generated images could reinforce these biases in the SE domain.</div></div><div><h3>Objective:</h3><div>In this paper, we focus on understanding the <em>implicit</em> bias exposed by general-purpose open-source image generation models towards SE tasks. In addition, we investigate the extent to which it is possible to mitigate the bias by using prompt engineering techniques.</div></div><div><h3>Methods:</h3><div>We perform an extensive empirical evaluation of the implicit <em>gender</em> and <em>ethnicity</em> bias exposed by six popular open-source image generation models towards SE tasks. We obtain 20,160 images by feeding each model with three sets of prompts describing different software-related tasks: One set does not include any specification of the person performing the task, one set specifies that the person performing the task is a <em>Software Engineer</em>, and the last set explicitly request a fair representation of different genders and ethnicities. Next, we evaluate the gender and ethnicity disparities in the generated images.</div></div><div><h3>Results:</h3><div>The results indicate that all models exhibit a significant bias related to gender and ethnicity in SE tasks. Furthermore, we demonstrate that prompt engineering effectively reduces gender bias in only one of the six models; however, none of the models achieves fair representation with respect to ethnicity.</div></div><div><h3>Conclusion:</h3><div>The results of our analysis highlight serious concerns about the adoption of these models to generate content for SE tasks and open the field for future research on bias mitigation in this context.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107956"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468019","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}
Pub Date : 2026-02-01Epub Date: 2025-10-31DOI: 10.1016/j.infsof.2025.107953
Rongzhi Qi , Jun Yang , Shuiyan Li , Yingchi Mao
Code summarization aims to generate comprehensive natural language descriptions for code snippets. Current methodologies predominantly focus on extracting syntactic and structural information from abstract syntax trees(ASTs). However, from a structural perspective, the numerous fine-grained nodes in an AST can lead to the capture of incomplete structural features. As a result, the generated summaries may suffer from redundancy. To address this limitation, we propose CSJSS (Code Summarization with Joint Structural Semantic of Abstract Syntax Trees), a novel code summarization model that enhances summarization by integrating the joint structural semantics of ASTs. CSJSS employs an encoder–decoder architecture. The Transformer-based encoder captures textual features of code token sequences. In addition, the joint encoder combines subtree-level recursive neural networks (S-RvNN) and global tree structural encoding (C-RvNN). It dynamically optimizes AST feature weights via graph attention mechanisms, thereby fusing local and global structural semantics. Extensive experiments conducted on the TLCodeSum and Funcom datasets demonstrate that CSJSS outperforms existing state-of-the-art models, achieving significant improvements in summarization quality. This research establishes a foundation for future advancements in code summarization and highlights the potential of integrating diverse feature representations to enhance summary accuracy.
{"title":"CSJSS: Augmenting code summarization with joint structural semantic of abstract syntax trees","authors":"Rongzhi Qi , Jun Yang , Shuiyan Li , Yingchi Mao","doi":"10.1016/j.infsof.2025.107953","DOIUrl":"10.1016/j.infsof.2025.107953","url":null,"abstract":"<div><div>Code summarization aims to generate comprehensive natural language descriptions for code snippets. Current methodologies predominantly focus on extracting syntactic and structural information from abstract syntax trees(ASTs). However, from a structural perspective, the numerous fine-grained nodes in an AST can lead to the capture of incomplete structural features. As a result, the generated summaries may suffer from redundancy. To address this limitation, we propose CSJSS (Code Summarization with Joint Structural Semantic of Abstract Syntax Trees), a novel code summarization model that enhances summarization by integrating the joint structural semantics of ASTs. CSJSS employs an encoder–decoder architecture. The Transformer-based encoder captures textual features of code token sequences. In addition, the joint encoder combines subtree-level recursive neural networks (S-RvNN) and global tree structural encoding (C-RvNN). It dynamically optimizes AST feature weights via graph attention mechanisms, thereby fusing local and global structural semantics. Extensive experiments conducted on the TLCodeSum and Funcom datasets demonstrate that CSJSS outperforms existing state-of-the-art models, achieving significant improvements in summarization quality. This research establishes a foundation for future advancements in code summarization and highlights the potential of integrating diverse feature representations to enhance summary accuracy.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107953"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520803","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}
Pub Date : 2026-02-01Epub Date: 2025-10-23DOI: 10.1016/j.infsof.2025.107932
Faith Culas, Amisha Singh, Atharva Arankalle, Priyanka Dhopade, Kelly Blincoe
Context:
Debugging is a critical practice in software engineering that enables software engineers to ensure the correctness of code by identifying and resolving bugs. It also benefits newcomers as it helps them go through the codebase, understand its structure, and learn about its functionality. Recent research has uncovered that some software engineering tools are not well suited to all ways of thinking, imposing an additional cognitive overhead on individuals whose cognitive styles are not well-supported by the tool. While biases have been explored in other software tools, little is known about whether debugging tools exhibit cognitive biases and introduce “inclusivity bugs”.
Objective:
This paper addresses this gap by examining inclusivity bugs that newcomers encounter when using the PyCharm debugger.
Methods:
In this study, we performed a controlled lab experiment where we observed 24 software engineering students with little to no experience as they used the PyCharm debugger for a set of tasks. We used a think-aloud protocol to capture participants’ thoughts throughout the experiment. Then, we conducted a thematic analysis, guided by our research question, to identify potential biases in the tool. We used the GenderMag framework to examine the relationship between cognitive style and the inclusivity bugs.
Results:
We detail our findings on 21 inclusivity bugs which are caused by 2 main reasons: discoverability and learnability. We identified trends that showed individuals with low self-efficacy, low motivation, risk-averse tendencies, and those who prefer to learn by processes and gather information selectively were the ones who faced the most challenges.
Conclusion:
The findings provide insights into how debuggers can be made more inclusive. They also highlight the need for continuous evaluation and adaptation of SE tools and practices to ensure they meet the needs of all users with diverse cognitive styles to ensure fairness.
{"title":"Newcomers’ experiences during debugging: A cognitive inclusivity perspective using GenderMag","authors":"Faith Culas, Amisha Singh, Atharva Arankalle, Priyanka Dhopade, Kelly Blincoe","doi":"10.1016/j.infsof.2025.107932","DOIUrl":"10.1016/j.infsof.2025.107932","url":null,"abstract":"<div><h3>Context:</h3><div>Debugging is a critical practice in software engineering that enables software engineers to ensure the correctness of code by identifying and resolving bugs. It also benefits newcomers as it helps them go through the codebase, understand its structure, and learn about its functionality. Recent research has uncovered that some software engineering tools are not well suited to all ways of thinking, imposing an additional cognitive overhead on individuals whose cognitive styles are not well-supported by the tool. While biases have been explored in other software tools, little is known about whether debugging tools exhibit cognitive biases and introduce “inclusivity bugs”.</div></div><div><h3>Objective:</h3><div>This paper addresses this gap by examining inclusivity bugs that newcomers encounter when using the PyCharm debugger.</div></div><div><h3>Methods:</h3><div>In this study, we performed a controlled lab experiment where we observed 24 software engineering students with little to no experience as they used the PyCharm debugger for a set of tasks. We used a think-aloud protocol to capture participants’ thoughts throughout the experiment. Then, we conducted a thematic analysis, guided by our research question, to identify potential biases in the tool. We used the GenderMag framework to examine the relationship between cognitive style and the inclusivity bugs.</div></div><div><h3>Results:</h3><div>We detail our findings on 21 inclusivity bugs which are caused by 2 main reasons: discoverability and learnability. We identified trends that showed individuals with low self-efficacy, low motivation, risk-averse tendencies, and those who prefer to learn by processes and gather information selectively were the ones who faced the most challenges.</div></div><div><h3>Conclusion:</h3><div>The findings provide insights into how debuggers can be made more inclusive. They also highlight the need for continuous evaluation and adaptation of SE tools and practices to ensure they meet the needs of all users with diverse cognitive styles to ensure fairness.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107932"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145371153","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}
Pub Date : 2026-02-01Epub Date: 2025-10-30DOI: 10.1016/j.infsof.2025.107948
Shaojian Qiu , Weibiao Chen , Haiyang Liu , Shaosheng Wang , Han Huang
Context:
Traditional pre-trained model for unit test generation often overlook structural code information during model fine-tuning, which limits the syntactic and semantic quality of generated test cases.
Objective:
This study aims to enhance the effectiveness of generating test cases by incorporating structural code representations into the fine-tuning process of pre-trained models.
Methods:
We propose Structure-Aware Fine-Tuning for Unit Test Generation (SF-UTG), an approach that integrates abstract syntax trees and data flow graphs into the fine-tuning phase of pre-trained model. By encoding hierarchical syntax and semantic dependencies, SF-UTG enables more structurally informed test generation.
Results:
Experiments on Defects4J and Methods2Test show that SF-UTG consistently outperforms baseline models. SF-UTG improves Compile Rate by 7.21%, Line Coverage by 2.05%, and Mutation Score by 2.01%, reflecting better syntactic correctness and fault detection. Its Parse Rate is within 2.54% of GPT-4, demonstrating strong test case validity. These results confirm SF-UTG’s effectiveness in generating accurate and reliable unit tests for real-world applications.
Conclusion:
The results validate the effectiveness of integrating AST and DFG structural information in fine-tuning and show that SF-UTG can be extended to other pre-trained models to further improve automated test generation.
{"title":"Boosting unit test generation via structure-aware fine-tuning of pre-trained model","authors":"Shaojian Qiu , Weibiao Chen , Haiyang Liu , Shaosheng Wang , Han Huang","doi":"10.1016/j.infsof.2025.107948","DOIUrl":"10.1016/j.infsof.2025.107948","url":null,"abstract":"<div><h3>Context:</h3><div>Traditional pre-trained model for unit test generation often overlook structural code information during model fine-tuning, which limits the syntactic and semantic quality of generated test cases.</div></div><div><h3>Objective:</h3><div>This study aims to enhance the effectiveness of generating test cases by incorporating structural code representations into the fine-tuning process of pre-trained models.</div></div><div><h3>Methods:</h3><div>We propose Structure-Aware Fine-Tuning for Unit Test Generation (SF-UTG), an approach that integrates abstract syntax trees and data flow graphs into the fine-tuning phase of pre-trained model. By encoding hierarchical syntax and semantic dependencies, SF-UTG enables more structurally informed test generation.</div></div><div><h3>Results:</h3><div>Experiments on Defects4J and Methods2Test show that SF-UTG consistently outperforms baseline models. SF-UTG improves Compile Rate by 7.21%, Line Coverage by 2.05%, and Mutation Score by 2.01%, reflecting better syntactic correctness and fault detection. Its Parse Rate is within 2.54% of GPT-4, demonstrating strong test case validity. These results confirm SF-UTG’s effectiveness in generating accurate and reliable unit tests for real-world applications.</div></div><div><h3>Conclusion:</h3><div>The results validate the effectiveness of integrating AST and DFG structural information in fine-tuning and show that SF-UTG can be extended to other pre-trained models to further improve automated test generation.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107948"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420369","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}
Pub Date : 2026-02-01Epub Date: 2025-10-29DOI: 10.1016/j.infsof.2025.107947
Haoran Shi , Ying Li , Jiabin Chen , Shijun Liu , Li Pan
Context:
With advancements in edge computing and open-source software technologies, computational demands in application scenarios have increased due to enhanced real-time situational awareness, decision-making capabilities, and flexible configurability. Traditional edge computing architectures remain constrained by cloud-service dependencies and struggle to meet the growing requirements of scenarios. The local edge–cloud architecture addresses these limitations by decentralizing cloud functionalities and effectively using device-side computational resources. However, heterogeneous nodes and uncertainties in optimized task scheduling introduce additional challenges.
Objectives:
To address the challenges within the local edge–cloud architecture, we propose a heterogeneous node representation and uncertainty handling approach.
Methods:
First, we define a hierarchical meta-model architecture centered on executing primitives and computing primitives to describe the static attributes and dynamic behaviors of heterogeneous devices. Second, we design model-to-code mapping rules to automate the generation of decision-making logic. We also introduce a Resource Description Model (RDM) and a Task Description Model (TDM) to facilitate resource allocation during decision processes. Finally, we categorize two types of uncertainties and propose two generalized strategies for uncertainty handling.
Results:
We conducted comparative experiments in simulated scenarios to evaluate the effectiveness and robustness of the proposed approach. Experimental results demonstrate that our approach effectively handles both process and requirements uncertainties in manufacturing through decision modeling and code generation, resulting in a 14.88% reduction in process execution time and a 95.18% decrease in machine suspension time. Furthermore, supported by the local edge–cloud architecture, our approach eliminates uncertainties associated with cloud–edge network communication.
Conclusion:
The proposed approach innovatively applies knowledge modeling to represent heterogeneous resources and handle uncertainties within the local edge–cloud architecture. It serves as a generalizable tool to guide the reliable and efficient process execution.
{"title":"A heterogeneous node representation and uncertainty handling approach under local edge–cloud architectures","authors":"Haoran Shi , Ying Li , Jiabin Chen , Shijun Liu , Li Pan","doi":"10.1016/j.infsof.2025.107947","DOIUrl":"10.1016/j.infsof.2025.107947","url":null,"abstract":"<div><h3>Context:</h3><div>With advancements in edge computing and open-source software technologies, computational demands in application scenarios have increased due to enhanced real-time situational awareness, decision-making capabilities, and flexible configurability. Traditional edge computing architectures remain constrained by cloud-service dependencies and struggle to meet the growing requirements of scenarios. The local edge–cloud architecture addresses these limitations by decentralizing cloud functionalities and effectively using device-side computational resources. However, heterogeneous nodes and uncertainties in optimized task scheduling introduce additional challenges.</div></div><div><h3>Objectives:</h3><div>To address the challenges within the local edge–cloud architecture, we propose a heterogeneous node representation and uncertainty handling approach.</div></div><div><h3>Methods:</h3><div>First, we define a hierarchical meta-model architecture centered on executing primitives and computing primitives to describe the static attributes and dynamic behaviors of heterogeneous devices. Second, we design model-to-code mapping rules to automate the generation of decision-making logic. We also introduce a Resource Description Model (RDM) and a Task Description Model (TDM) to facilitate resource allocation during decision processes. Finally, we categorize two types of uncertainties and propose two generalized strategies for uncertainty handling.</div></div><div><h3>Results:</h3><div>We conducted comparative experiments in simulated scenarios to evaluate the effectiveness and robustness of the proposed approach. Experimental results demonstrate that our approach effectively handles both process and requirements uncertainties in manufacturing through decision modeling and code generation, resulting in a 14.88% reduction in process execution time and a 95.18% decrease in machine suspension time. Furthermore, supported by the local edge–cloud architecture, our approach eliminates uncertainties associated with cloud–edge network communication.</div></div><div><h3>Conclusion:</h3><div>The proposed approach innovatively applies knowledge modeling to represent heterogeneous resources and handle uncertainties within the local edge–cloud architecture. It serves as a generalizable tool to guide the reliable and efficient process execution.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107947"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420381","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}
Pub Date : 2026-02-01Epub Date: 2025-10-27DOI: 10.1016/j.infsof.2025.107946
Oleksandr Kosenkov , Ehsan Zabardast , Davide Fucci , Daniel Mendez , Michael Unterkalmsteiner
Context:
Consistent requirements and system specifications are essential for the compliance of software systems towards the General Data Protection Regulation (GDPR). Both artefacts need to be “grounded” in the original text and conjointly assure the achievement of privacy by design (PbD).
Objectives:
There is little understanding of the perspectives of practitioners on specification objectives and goals to address PbD. Existing approaches to GDPR and PbD do not account for the complex intersection between problem and solution space expressed in GDPR. In this study we explore the demand for conjoint requirements and system specification for PbD and suggest an initial version of an approach to address this demand.
Methods:
We reviewed existing secondary and related primary studies on GDPR compliance and conducted interviews with practitioners to (1) investigate the state-of-practice in requirements and system specifications for GDPR compliance and (2) understand the underlying specification objectives and goals (e.g., traceability). We developed and evaluated an initial version of an approach for requirements and systems specification for PbD, and evaluated it against the specification objectives.
Results:
The relationship between problem and solution space, as expressed in GDPR, is instrumental in supporting PbD. We demonstrate how our approach, based on the modeling GDPR content with original legal concepts, contributes to specification objectives of capturing legal knowledge, supporting specification transparency for roles involved, and traceability.
Conclusion:
In addition to assuring traceability, GDPR demands need to be addressed throughout different levels of abstraction in the engineering lifecycle to achieve PbD. Legal knowledge specified in the GDPR text should be captured in specifications to address the demands of different stakeholders and ensure compliance. While our results confirm the suitability of our approach to address practical needs, we also revealed specific needs for the future effective operationalization of our suggested approach.
{"title":"Privacy by design: Aligning GDPR and software engineering specifications with a requirements engineering approach","authors":"Oleksandr Kosenkov , Ehsan Zabardast , Davide Fucci , Daniel Mendez , Michael Unterkalmsteiner","doi":"10.1016/j.infsof.2025.107946","DOIUrl":"10.1016/j.infsof.2025.107946","url":null,"abstract":"<div><h3>Context:</h3><div>Consistent requirements and system specifications are essential for the compliance of software systems towards the General Data Protection Regulation (GDPR). Both artefacts need to be “grounded” in the original text and conjointly assure the achievement of privacy by design (PbD).</div></div><div><h3>Objectives:</h3><div>There is little understanding of the perspectives of practitioners on specification objectives and goals to address PbD. Existing approaches to GDPR and PbD do not account for the complex intersection between problem and solution space expressed in GDPR. In this study we explore the demand for conjoint requirements and system specification for PbD and suggest an initial version of an approach to address this demand.</div></div><div><h3>Methods:</h3><div>We reviewed existing secondary and related primary studies on GDPR compliance and conducted interviews with practitioners to (1) investigate the state-of-practice in requirements and system specifications for GDPR compliance and (2) understand the underlying specification objectives and goals (e.g., traceability). We developed and evaluated an initial version of an approach for requirements and systems specification for PbD, and evaluated it against the specification objectives.</div></div><div><h3>Results:</h3><div>The relationship between problem and solution space, as expressed in GDPR, is instrumental in supporting PbD. We demonstrate how our approach, based on the modeling GDPR content with original legal concepts, contributes to specification objectives of capturing legal knowledge, supporting specification transparency for roles involved, and traceability.</div></div><div><h3>Conclusion:</h3><div>In addition to assuring traceability, GDPR demands need to be addressed throughout different levels of abstraction in the engineering lifecycle to achieve PbD. Legal knowledge specified in the GDPR text should be captured in specifications to address the demands of different stakeholders and ensure compliance. While our results confirm the suitability of our approach to address practical needs, we also revealed specific needs for the future effective operationalization of our suggested approach.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107946"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420379","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}
Pub Date : 2026-02-01Epub Date: 2025-11-03DOI: 10.1016/j.infsof.2025.107952
Katharina Müller , Nikolay Harutyunyan , Dirk Riehle , Christian Koch
Due to the COVID-19 pandemic that broke out in 2020, companies switched to working from home on a large scale. Now, in 2025, many employees are working from home entirely or are only in the office irregularly. This has created a new working environment for many software professionals that resembles both the distributed software development (DSD) and open source software development (OSS) models. While working from home or a hybrid work environment is relatively new for many organizations (about 5 years), the challenges in DSD and OSS development have already been widely researched. Our research question focuses on best practices derived from OSS and DSD that solve the current challenges of working from home. We conducted a qualitative survey and interviewed fifteen individuals who have experience with DSD or OSS development. In this paper, we present fourteen proposed best practices for remote communication, collaboration, management, and tooling. Using the context–problem–solution pattern, we go beyond presenting high-level practices and suggest actionable details. The results of the study provide insights into this topic with high industry relevance. At the same time, the study contributes to existing academic research on working from home and the consequences of the COVID-19 pandemic.
{"title":"Best practices for work from home: A qualitative survey in open source and distributed software development","authors":"Katharina Müller , Nikolay Harutyunyan , Dirk Riehle , Christian Koch","doi":"10.1016/j.infsof.2025.107952","DOIUrl":"10.1016/j.infsof.2025.107952","url":null,"abstract":"<div><div>Due to the COVID-19 pandemic that broke out in 2020, companies switched to working from home on a large scale. Now, in 2025, many employees are working from home entirely or are only in the office irregularly. This has created a new working environment for many software professionals that resembles both the distributed software development (DSD) and open source software development (OSS) models. While working from home or a hybrid work environment is relatively new for many organizations (about 5 years), the challenges in DSD and OSS development have already been widely researched. Our research question focuses on best practices derived from OSS and DSD that solve the current challenges of working from home. We conducted a qualitative survey and interviewed fifteen individuals who have experience with DSD or OSS development. In this paper, we present fourteen proposed best practices for remote communication, collaboration, management, and tooling. Using the context–problem–solution pattern, we go beyond presenting high-level practices and suggest actionable details. The results of the study provide insights into this topic with high industry relevance. At the same time, the study contributes to existing academic research on working from home and the consequences of the COVID-19 pandemic.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"190 ","pages":"Article 107952"},"PeriodicalIF":4.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520802","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}
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