IEEE Transactions on Software Engineering最新文献

{"title":"Mining Feature Requests and Bug Reports from Tweets and App Store Reviews: Are We There Yet? Insights from a Replication Study","authors":"Cheshta Sharma, Michael Svoboda, Nikhil Sharma, Shanzi Ye, Rahul Ravi, Faezehsadat Shahidi, Gouri Ginde","doi":"10.1109/tse.2026.3743853","DOIUrl":"https://doi.org/10.1109/tse.2026.3743853","url":null,"abstract":"","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"13 1","pages":"1-21"},"PeriodicalIF":7.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149218696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"LLM-based Vulnerability Detection at Project Scale: An Empirical Study","authors":"Fengjie Li, Jiajun Jiang, Dongchi Chen, Yingfei Xiong","doi":"10.1109/tse.2026.3742453","DOIUrl":"https://doi.org/10.1109/tse.2026.3742453","url":null,"abstract":"","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"11 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149152239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"SELU: A Software Engineering Language Understanding Benchmark","authors":"Fabian C. Pena, Steffen Herbold","doi":"10.1109/tse.2026.3741144","DOIUrl":"https://doi.org/10.1109/tse.2026.3741144","url":null,"abstract":"","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"21 1","pages":"1-13"},"PeriodicalIF":7.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149152240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"EmpathiSEr: Validated Scales for Measuring Empathy Towards Software Engineering Practitioners and Users","authors":"Hashini Gunatilake, John Grundy, Rashina Hoda, Ingo Mueller","doi":"10.1109/tse.2026.3740594","DOIUrl":"https://doi.org/10.1109/tse.2026.3740594","url":null,"abstract":"","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"21 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"LogLLM: Log-based Anomaly Detection Using Large Language Models","authors":"Wei Guan, Jian Cao, Shiyou Qian, Jianqi Gao, Chun Ouyang","doi":"10.1109/tse.2026.3740394","DOIUrl":"https://doi.org/10.1109/tse.2026.3740394","url":null,"abstract":"","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"1 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models","authors":"Ahmadreza Saboor Yaraghi;Golnaz Gharachorlu;Sakina Fatima;Lionel C. Briand;Ruiyuan Wan;Ruifeng Gao","doi":"10.1109/TSE.2026.3707426","DOIUrl":"10.1109/TSE.2026.3707426","url":null,"abstract":"Fault localization (FL) is a critical step in debugging, which typically relies on repeated executions to pinpoint faulty code regions. However, repeated executions can be impractical in the presence of non-deterministic failures or high execution costs. While recent efforts have leveraged Large Language Models (LLMs) to aid execution-free FL, these have primarily focused on identifying faults in the system-under-test (SUT) rather than in the often complex system-level test code, i.e., the test script for system-level testing. However, the latter is also important, as in practice, many failures are triggered by faulty test code. To overcome these challenges, we introduce a fully static, LLM-driven approach for system-level test code fault localization (TCFL) that does not require executing the test case. Our method uses a single failure execution log to estimate the test’s execution trace through three novel algorithms that identify only code statements likely involved in the failure. This pruned trace, combined with the error message, is used to prompt the LLM to rank potential faulty locations. Our black-box, system-level approach requires no access to the SUT source code and is applicable to complex test scripts that assess full system behavior. We evaluate our technique at the function, block, and line levels using an industrial dataset of faulty Python test cases that were not used in pre-training LLMs. Results show that our best-estimated traces closely match the actual traces, with an F1 score of around 90%. Additionally, pruning the complex system-level test code reduces the LLM’s inference time by up to 34% without any loss in FL performance. Our results further suggest that block-level TCFL offers a practical balance, narrowing the search space while preserving useful context, achieving an 81% hit rate at top-3 (Hit@3). Finally, our TCFL method achieves equal or higher FL accuracy, requiring over 85% less average inference time per test case and 93% fewer tokens than the latest LLM-guided FL method.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"2986-3018"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148342297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Automated Property-Based Fuzzing for Finding Data Manipulation Errors in Android Apps","authors":"Jingling Sun;Ting Su;Xixian Liang;Jue Wang;Geguang Pu;Zhendong Su","doi":"10.1109/TSE.2026.3704631","DOIUrl":"10.1109/TSE.2026.3704631","url":null,"abstract":"Like many software applications, <italic>data manipulation functionalities</i> (DMFs) are prevalent in Android apps, which perform the common CRUD operations (<italic>create</i>, <italic>read</i>, <italic>update</i>, <italic>delete</i>) to handle app-specific data. Thus, ensuring the correctness of these DMFs is fundamentally important for many core app functionalities. However, the bugs related to DMFs (named as <italic>data manipulation errors</i>, DMEs), especially those <italic>non-crashing logic ones</i>, are prevalent but difficult to find. To this end, inspired by property-based testing, we introduce a <italic>property-based fuzzing</i> approach to effectively finding DMEs in Android apps. Our <italic>key</i> idea is that, we randomly interleave different DMFs and other possible events to explore <italic>diverse</i> app states for thorough validation. Specifically, our approach characterizes DMFs in (data) model-based properties and leverages the consistency between the data model and the UI layouts as the handler to do property checking. DMFs are automatically identified by analyzing execution traces that exhibit common DMF characteristics, enabling the synthesis of DMF properties across apps. To support the application of our approach, we implemented an automated GUI testing tool, <sc>PBFDroid+</small>. In evaluation, <sc>PBFDroid+</small> automatically identified 146 correct and unique DMFs in 20 real-world apps with an accuracy of 92.4%. Based on these DMFs, it further detected 32 unique and previously unknown bugs in these apps. Out of the 32 bugs, 23 are non-crashing logic bugs, and 9 are crash ones. To date, 24 have been confirmed, of which 15 have already been fixed. Many of these bugs are non-trivial and lead to different types of app failures. Our further evaluation confirms that <italic>none</i> of the 23 non-crashing DMEs can be found by the state-of-the-art techniques. Overall, <sc>PBFDroid+</small> enables precise and automated discovery of non-trivial DMEs by automatically identifying DMFs and verifying their properties. We have made all the artifacts publicly available at: <uri>https://zenodo.org/records/19951139</uri>.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"2875-2893"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"LENA: Llama-Based Embeddings of Neutralized Assembly for Cross-Compiler/Optimization Binary Code Similarity Detection","authors":"Mohammadhossein Amouei;Benjamin C. M. Fung;Philippe Charland;Jun Meng","doi":"10.1109/TSE.2026.3705321","DOIUrl":"10.1109/TSE.2026.3705321","url":null,"abstract":"Binary code similarity detection (BCSD) is fundamental to software security, yet remains challenging under cross-compiler and cross-optimization settings. Existing methods often rely on contrastive objectives that are sensitive to false negatives, while recent alternatives may learn redundant embeddings that limit fine-grained discrimination. In this paper, we present LENA, a BCSD framework that adapts a Llama backbone through an optimization-translation task and trains a lightweight projector using a hybrid objective that combines contrastive learning with variance and covariance regularization. Experiments on x86-64 benchmarks show that LENA outperforms state-of-the-art baselines, improving average Recall@1 by 10.45% on GCC, 5.36% on Clang, and 8.55% in cross-compiler settings, while also improving average R-Precision in vulnerability detection. We further provide a supporting tie-aware evaluation analysis and introduce Tie-sensitive Reciprocal Rank (TsRR), which provides deterministic reciprocal-rank-based reporting when tied similarity scores occur and reduces ambiguity in BCSD evaluation.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"2946-2969"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Spotting Setting-Related UI Display Bugs in Android Apps","authors":"Huaxun Huang;Wu Liu;Jiahao Gu;Rongxin Wu","doi":"10.1109/TSE.2026.3709984","DOIUrl":"https://doi.org/10.1109/TSE.2026.3709984","url":null,"abstract":"Android provides a wide range of system settings that allow users to control the runtime behaviors of apps, such as screen rotation and UI display. However, setting-related bugs occur when developers do not fully align their apps with the extensive range of system settings that users can define. These bugs can commonly affect apps’ UI, causing setting-related UI display (SUD) bugs that negatively impact user experience. While existing research has explored automated detection of SUD bugs, these approaches often suffer from false negatives. This limitation stems from an incomplete understanding of how app components should adapt UI elements to diverse system settings. To address this gap, we conducted an empirical study to identify common patterns of unexpected setting adaptations that result in SUD bugs. These patterns then served as the test oracle for our proposed automated tool, SUDFinder. To ensure the test coverage, SUDFinder injects a test activity to visually render the XML configuration files of each UI page. We evaluated SUDFinder on 29 popular, open-source apps on F-Droid and found that it effectively identifies 98 previously unknown SUD bugs, achieving a precision of 0.76. So far, 67 have been confirmed and 37 have been fixed by the app developers.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"3129-3145"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Efficient and Universal Watermarking for LLM-Generated Code Detection","authors":"Boquan Li;Zirui Fu;Mengdi Zhang;Peixin Zhang;Jun Sun;Xingmei Wang","doi":"10.1109/TSE.2026.3710556","DOIUrl":"10.1109/TSE.2026.3710556","url":null,"abstract":"Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plug-and-play watermarking approach for AI-generated code detection, named <bold>ACW</b> (<bold>A</b>I <bold>C</b>ode <bold>W</b>atermarking). <bold>ACW</b> is training-free and works by selectively applying a set of carefully-designed, semantic-preserving and idempotent code transformations to LLM code outputs. The presence or absence of the transformations serves as implicit watermarks, enabling the detection of AI-generated code. Our experimental results show that <bold>ACW</b> effectively and efficiently detects AI-generated code, preserves code utility, and is resilient against potential code disruptions. Especially, <bold>ACW</b> is universal across different LLMs, addressing the limitations of existing approaches.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"52 10","pages":"3198-3213"},"PeriodicalIF":6.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148401118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}