bluuebunny/crossref_metadata_2025 huggingface dataset:
Quick Prediction of Complex Temperature Fields Using Conditional Generative Adversarial Networks
Authors: Wu Xiaohua , Lu Longsheng , Liang Lanzhi , Mei Xiaokang , Liang Qinghua , Zhong Yilin , Huang Zeqiang , Yang Shu , He Hengfei , Xie Yingxi
Published: July 2024

Qualified thermal management is an important guarantee for the stable work of electronic devices. However, the increasingly complex cooling structure needs several hours or even longer to simulate, which hinders finding the optimal heat dissipation design in the limited space. Herein, an approach based on conditional generative adversarial network (cGAN) is reported to bridge complex geometry and physical field. The established end-to-end model not only predicted the maximum temperature with high precision but also captured real field details in the generated image. The impact of amount of training data on model prediction performance was discussed, and the performance of the models fine-tuned and trained from scratch was also compared in the case of less training data or using in new electronic devices. Furthermore, the high expansibility of geometrically encoded labels makes this method possible to be used in the heat dissipation analysis of more electronic devices. More importantly, this approach, compared to the grid-based simulation, accelerates the process by several orders of magnitude and saves a large amount of energy, which can vastly improve the efficiency of the thermal management design of electronic devices.
Smoke-charged vortex doubles hemispheric aerosol in the middle stratosphere and buffers ozone depletion
Authors: Ma Chaoqun , Su Hang , Lelieveld Jos , Randel William , Yu Pengfei , Andreae Meinrat O. , Cheng Yafang
Published: July 2024

Australian mega-wildfires in the summer of 2019-2020 injected smoke into the stratosphere, causing strong ozone depletion in the lower stratosphere. Here, we model the smoke plume and reproduce its unexpected trajectory toward the middle stratosphere at ~35-kilometer altitude. We show that a smoke-charged vortex (SCV) induced and maintained by absorbing aerosols played a key role in lofting pollutants from the lower stratosphere and nearly doubled the southern hemispheric aerosol burden in the middle stratosphere. The SCV caused a redistribution of stratospheric aerosols, which boosted heterogeneous chemistry in the middle stratosphere and enhanced ozone production, compensating for up to 70% of the ozone depletion in the lower stratosphere. As global warming continues, we expect a growing frequency and importance of SCVs in promoting the impacts of wildfires on stratospheric aerosols and chemistry.
Generative AI enhances individual creativity but reduces the collective diversity of novel content
Authors: Doshi Anil R. , Hauser Oliver P.
Published: July 2024

Creativity is core to being human. Generative artificial intelligence (AI)—including powerful large language models (LLMs)—holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI–enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: With generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity.
Direct flipping dynamics and quantized enrichment of chirality at single-molecule resolution
Authors: Hu Weilin , Zhang Zhiyun , Xiong Wan , Li Mingyao , Yan Yong , Yang Caiyao , Zou Qi , Lü Jing-Tao , Tian He , Guo Xuefeng
Published: July 2024

Chirality is an important aspect of nature, and numerous macroscopic methods have been developed to understand and control chirality. For the chiral tertiary amines, their flexible flipping process makes it possible to achieve high chiral controllability without bond formation and breaking. Here, we present a type of stable chiral single-molecule devices formed by tertiary amines, using graphene-molecule-graphene single-molecule junctions. These single-molecule devices allow real-time, in situ, and long-time measurements of the flipping process of an individual chiral nitrogen center with high temporal resolution. Temperature- and bias voltage–dependent experiments, along with theoretical investigations, revealed diverse chiral intermediates, indicating the regulation of the flipping dynamics by energy-related factors. Angle-dependent measurements further demonstrated efficient enrichment of chiral states using linearly polarized light by a symmetry-related factor. This approach offers a reliable means for understanding the chirality’s origin, elucidating microscopic chirality regulation mechanisms, and aiding in the design of effective drugs.
A confusion of pathways: Discerning cell death mechanisms in SARS-CoV-2 infection
Authors: Wong Lok-Yin Roy , Perlman Stanley
Published: July 2024

Upon SARS-CoV-2 infection, infected cells undergo necroptosis, whereas delayed apoptosis and pyroptosis occur in uninfected, bystander cells, thus providing a plausible explanation for the extensive injury among myriad uninfected cells.
Trajectories in long-term condition accumulation and mortality in older adults: a group-based trajectory modelling approach using the English Longitudinal Study of Ageing
Authors: Chalitsios Christos V , Santoso Cornelia , Nartey Yvonne , Khan Nusrat , Simpson Glenn , Islam Nazrul , Stuart Beth , Farmer Andrew , Dambha-Miller Hajira
Published: July 2024

Objectives

To classify older adults into clusters based on accumulating long-term conditions (LTC) as trajectories, characterise clusters and quantify their associations with all-cause mortality.

Design

We conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models.

Results

Five distinct clusters of accumulating LTC trajectories were identified and characterised as: ‘no LTC’ (18.57%), ‘single LTC’ (31.21%), ‘evolving multimorbidity’ (25.82%), ‘moderate multimorbidity’ (17.12%) and ‘high multimorbidity’ (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the ‘high multimorbidity’ cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the ‘no LTC’ cluster.

Conclusions

The development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.
Did high frequency phone surveys during the COVID-19 pandemic include disability questions? An assessment of COVID-19 surveys from March 2020 to December 2022
Authors: Nascimento Dial Amanda , Vicente David , Mitra Sophie , Teodoro Douglas , Rivas Velarde Minerva
Published: July 2024

Objectives

In the midst of the pandemic, face-to-face data collection for national censuses and surveys was suspended due to limitations on mobility and social distancing, limiting the collection of already scarce disability data. Responses to these constraints were met with a surge of high-frequency phone surveys (HFPSs) that aimed to provide timely data for understanding the socioeconomic impacts of and responses to the pandemic. This paper provides an assessment of HFPS datasets and their inclusion of disability questions to evaluate the visibility of persons with disabilities during the COVID-19 pandemic.

Design

We collected HFPS questionnaires conducted globally from the onset of the pandemic emergency in March 2020 until December 2022 from various online survey repositories. Each HFPS questionnaire was searched using a set of keywords for inclusion of different types of disability questions. Results were recorded in an Excel review log, which was manually reviewed by two researchers.

Methods

The review of HFPS datasets involved two stages: (1) a main review of 294 HFPS dataset-waves and (2) a semiautomated review of the same dataset-waves using a search engine-powered questionnaire review tool developed by our team. The results from the main review were compared with those of a sensitivity analysis using and testing the tool as an alternative to manual search.

Results

Roughly half of HFPS datasets reviewed and 60% of the countries included in this study had some type of question on disability. While disability questions were not widely absent from HFPS datasets, only 3% of HFPS datasets included functional difficulty questions that meet international standards. The search engine-powered questionnaire review tool proved to be able to streamline the search process for future research on inclusive data.

Conclusions

The dearth of functional difficulty questions and the Washington-Group Short Set in particular in HFPS has contributed to the relative invisibility of persons with disabilities during the pandemic emergency, the lingering effects of which could impede policy-making, monitoring and advocacy on behalf of persons with disabilities.
DNA barcoding of southern African mammal species and construction of a reference library for forensic application
Authors: Baxter J.R. , Kotze A. , de Bruyn M. , Matlou K. , Labuschagne K. , Mwale M.
Published: July 2024

Combating wildlife crimes in South Africa requires accurate identification of traded species and their products. Diagnostic morphological characteristics needed to identify species are often lost when specimens are processed and customs officials lack the expertise to identify species. As a potential solution, DNA barcoding can be used to identify morphologically indistinguishable specimens in forensic cases. However, barcoding is hindered by the reliance on comprehensive, validated DNA barcode reference databases, which are currently limited. To overcome this limitation, we constructed a barcode library of cytochrome c oxidase subunit 1 and cytochrome b sequences for threatened and protected mammals exploited in southern Africa. Additionally, we included closely related or morphologically similar species and assessed the database’s ability to identify species accurately. Published southern African sequences were incorporated to estimate intraspecific and interspecific variation. Neighbor-joining trees successfully discriminated 94%–95% of the taxa. However, some widespread species exhibited high intraspecific distances (>2%), suggesting geographic sub-structuring or cryptic speciation. Lack of reliable published data prevented the unambiguous discrimination of certain species. This study highlights the efficacy of DNA barcoding in species identification, particularly for forensic applications. It also highlights the need for a taxonomic re-evaluation of certain widespread species and challenging genera.
Structural Transformation in Asia and the Pacific
Authors: GIBSON JOHN
Published: July 2024

Many countries in Asia and the Pacific have experienced rapid economic growth and structural transformation in recent decades. Yet, some countries are still at an early stage of this structural transformation and face external conditions less favorable than those faced by the first movers when they were at a similar early stage in their transformation. The external environment is less auspicious, with trade tensions and “friend-shoring” leading to possible deglobalization, while demographic headwinds could also lower sustainable growth rates and induce technological changes, such as the increased use of robots, that reduce the possibility of relying upon labor-intensive development strategies.
Shifting Trade Winds: Southeast Asia’s Response to the United States–People’s Republic of China Trade Dispute
Authors: LIM SUNGHUN , NGUYEN ANH PHUOC THIEN
Published: July 2024

This study delves into the trade dynamics of Southeast Asian countries in response to the trade dispute between the United States (US) and the People’s Republic of China (PRC). Our analysis uncovers diverse patterns of trade diversion effects among eight Southeast Asian countries, revealing significant disparities in their reactions to this trade dispute. Specifically, we observe Viet Nam’s substantial export growth to both the US and the PRC, influenced by geopolitical uncertainties and strategic relocations. Thailand, on the other hand, experiences positive effects on its exports to the US, potentially due to trade diversion, alongside diminishing exports to the PRC. Moreover, we find sector-specific trends, such as an upsurge in machinery exports from Viet Nam, Thailand, and Indonesia. In contrast, Brunei Darussalam, Indonesia, Malaysia, Myanmar, the Philippines, and Singapore largely sustained their respective export levels to the US. Our findings highlight the importance of tailored trade policies that consider each Southeast Asian country’s unique industrial structure and degree of global value chain integration.
Overcoming the Limitations of Learning-Based VQA for Counting Questions with Zero-Shot Learning
Authors: Lubna A. , Kalady Saidalavi
Published: July 2024

Visual question answering (VQA) research has garnered increasing attention in recent years. It is considered a visual Turing test because it requires a computer to respond to textual questions based on an image. Expertise in computer vision, natural language processing, knowledge understanding, and reasoning is required to solve the problem of VQA. Most techniques employed for VQA consist of models that are developed to learn the combination of image and question features along with the expected answer. The techniques chosen for image and question feature extraction and combining the features change with each model. This method of teaching a model of the question–answer pattern is ineffective for queries that involve counting and reasoning. This approach also requires considerable resources and large datasets for the training. The general VQA datasets feature a restricted number of items as responses to counting questions ([Formula: see text]), and the distribution of the answers is not uniform. To investigate these issues in VQA, we created synthetic datasets that could be modified to adjust the number of objects in the image and the amount of occlusion. Specifically, a zero-shot learning VQA system was devised for counting-related questions that provide answers by analyzing the output of an object detector and the query keywords. Using synthetic datasets, our model generated 100% correct results. Testing on the benchmark datasets task directed image understanding challenge (TDIUC) and TallyQA-simple indicated that the proposed model matched the performance of the learning-based baseline models. This methodology can be used efficiently for counting VQA questions confined to certain domains when the number of items to be counted is significant.
RSM-BASED STATISTICAL APPROACH TO ENHANCE THE COMPRESSIVE PERFORMANCE OF ABS P400 PARTS FABRICATED VIA FDM TECHNOLOGY
Authors: EQUBAL AZHAR , EQUBAL MD. ASIF , AHMAD RABNAWAZ , KUMAR MANOJ , KHAN OSAMA , YAHYA ZEINEBOU , ALHODAIB AIYESHAH , YADAV ASHOK KUMAR
Published: July 2024

This research investigates the performance of ABS P400 parts under compressive loading. These ABS P400 parts are fabricated by fused deposition modeling (FDM) process using omnidirectional printing. The effects of three critical parameters, raster angle (A), air gap (B), and raster width (C), each at three different levels, were analyzed on three different performance measures, i.e. total deformation up to failure ([Formula: see text]) measured in %, maximum energy ([Formula: see text]) in MJ/m3and break energy ([Formula: see text]) in MJ/m3. Results of experimentations were analyzed using analysis of variance (ANOVA), perturbation curve and 3D surface plots. The research established the anisotropic nature, durability and less brittleness of the FDM fabricated ABSP400 part, which leads to lower strength and also influence the energy absorbing behavior while deformation under compression. Complex relationships were exhibited between the chosen parameters and studied measures. It was also observed that [Formula: see text] is noticeable due to less brittleness of ABS P400 however, [Formula: see text] and [Formula: see text] vary in a contradictory fashion with changes in process parameters. The models were validated using normality plots. Multi-objective optimization was done using the desirability approach to determine the optimal combination of FDM process parameters for optimum responses. The desirability result showed that the optimized input parameter is [Formula: see text][Formula: see text]mm, and [Formula: see text][Formula: see text]mm which yield maximized optimum responses as [Formula: see text]%, [Formula: see text][Formula: see text]MJ/m3, and [Formula: see text][Formula: see text]MJ/m3. The work not only reflects the effect of three FDM parameters on the total deformation up to failure ([Formula: see text] in %) but, in addition, energy absorption behavior of FDM parts under compression is also investigated. The study on the behavior of energy at ultimate stress or ultimate load and breaking stress or breaking load were rarely investigated in earlier research which reflects the novelty of our research.
Fund Flows and Fund Performance on Lottery Funds
Authors: Chen Chun-Yen , Chen Hong-Yi , Chen Hsiao-Yin
Published: July 2024

This study introduces a new indicator to measure the lottery-like degree of financial products by considering their idiosyncratic volatility and skewness. The findings indicate that investors tend to purchase funds that hold lottery-like stocks, while investors prefer lottery-like funds when the market sentiment is high. However, funds holding lottery-like stocks and those exhibiting lottery traits experience relatively lower adjusted returns in the next quarter. Interestingly, when the market sentiment is high, investors can obtain better performance only from funds that hold lottery-like stocks, indicating that investors may be better off selecting lottery-holding funds rather than lottery-like funds during periods of high sentiment. Overall, our study sheds light on investor behavior and the impact of market sentiment on fund flows and performance in lottery-like funds.
Viability for impulsive stochastic differential inclusions driven by fractional Brownian motion
Authors: Trong Nguyen Ngoc , Truong Le Xuan , Do Tan Duc
Published: July 2024

In this paper, we prove necessary and sufficient conditions for the viability for an impulsive stochastic functional differential inclusion driven by a fractional Brownian motion. The viable property is of interest since it reflects the stability of the model under consideration. The fractional Brownian motion provides a memory effect to the model. Whereas, the appearance of the impulsive factor introduces jumps to the solutions and is new to the analysis of this type. Hence our results are new even in the special case of stochastic differential equation setting.
Enhanced Nonenzymatic Glucose Biosensor of ZnO Nanostructure via Nonthermal Plasma
Authors: Mohammed Raneen Qasem , Ahmed Baida M.
Published: July 2024

The sensitive nonenzymatic sensing of glucose has been made feasible for the first time using a nonthermal plasma (NTP)-synthesized ZnO nanostructure. This work presents an interesting and novel method for surface modification. The effects of flow gas of Argon/Oxygen (20, 30 and 40 l/min) on the Zn foil surface and various properties were investigated. Optical emission spectroscopy OES has been used to characterize transmissions for positive and negative systems of Ar/O plasma. X-ray diffraction showed close adjacent tops and the strongest peak at Zn (101). EDX displays the constants (Ok_[Formula: see text], (Znk_[Formula: see text] and (Znk_[Formula: see text] and change in (ZnL_[Formula: see text]. Photoluminescence (PL) Analysis shows a shift at the vertex toward the left and then toward the right confirming the reaction of all PL spectra within a strong UV emission peak. Raman spectroscopy analysis demonstrates two clear peaks gradually shifting to the right with an increase in the percentage of gas flowing compared to the blank metal, and scanning electron microscopy (FE-SEM) images show changes in the shape of nanoparticles due to increasing gas flow, the surface of zinc metal is affected by cold plasma and forms particles with very small diameters ranging from 10.8[Formula: see text]nm to 123[Formula: see text]nm. Also altered the surface morphology where the nano shape changed from flower-like particles to sheets with diameters ranging 282.7–643.5[Formula: see text]nm and the sheets grew with the increase of gas flow to diameters ranging 132–710[Formula: see text]nm. ZnO nanostructures are employed as biosensor electrodes (nonenzymatic) glucose as the increase in current is proportional to the flowing gas (2.06E-03[Formula: see text]mA, 2.09E-03[Formula: see text]mA and 4.34E-03[Formula: see text]mA).
Tear Fracture Analysis of Fiber-Reinforced Conducting Polymer-based Soft Actuator
Authors: Ghosh Saswath , Gour Sankalp , Kumar Deepak , Roy Sitikantha
Published: July 2024

Conducting polymer (CP) is an electroactive polymer that displays specific electronic properties, including conductivity. The utility of CP-based soft actuators in various biomedical applications has recently been motivated due to their low voltage-driven specialty compared to the widely used high voltage-driven dielectric elastomers. In some biomedical applications, highly delicate CP actuators may be torn or damaged for unknown reasons. In this regard, this study develops a tear fracture model for fiber-reinforced CP actuators to investigate a specific fracture test of mode III, namely, the trousers test, which involves pulling two legs of a cut specimen horizontally apart. The development of the tear fracture model adopts a well-known Griffith criterion along with the thermodynamically consistent continuum mechanics approach. Additionally, prominent strain energy capturing elastomer strain-stiffening at a moderate strain range is used in conjunction with an empirically established correlation to couple the two internal phenomena, ion diffusion and mechanical deformation of the CP actuators. Later, the effects of various electrical and geometrical parameters on the tearing of the actuator are also addressed.
p-Adic hypergeometric functions and the trace of Frobenius of elliptic curves
Authors: Sulakashna  , Barman Rupam
Published: July 2024

Let p be an odd prime and [Formula: see text], [Formula: see text]. For positive integers n, let [Formula: see text] denote McCarthy’s p-adic hypergeometric function. In this paper, we prove an identity expressing a [Formula: see text] hypergeometric function as a sum of two [Formula: see text] hypergeometric functions. This identity generalizes some known identities satisfied by the finite field hypergeometric functions. We also prove a transformation that relates [Formula: see text] and [Formula: see text] hypergeometric functions. Next, we express the trace of Frobenius of elliptic curves in terms of special values of [Formula: see text] and [Formula: see text] hypergeometric functions. Our results extend the recent works of Tripathi and Meher on the finite field hypergeometric functions to wider classes of primes.
On the Shintani lifting of integral weight modular forms to half-integral weight modular forms
Authors: Zhang Di
Published: July 2024

In this paper, we explore the adelic Shintani lifting of a weight 2 cusp form f. For a finite Schwartz function related to an auxiliary quadratic character [Formula: see text], we express certain Fourier coefficients of this lifting in terms of the twisted L-value [Formula: see text].
Pairs of cyclic cubic units with rational difference
Authors: Komatsu Toru
Published: July 2024

In this paper, we determine all the pairs of cubic units in cyclic cubic fields of which differences are nonzero rational. Consequently we show that for a cyclic cubic field, it is simplest if and only if it has such a pair of units.
Eigenspaces of newforms with nontrivial character
Authors: Karameris Markos
Published: July 2024

Let [Formula: see text] denote the space of holomorphic cuspforms with Dirichlet character [Formula: see text] and modular subgroup [Formula: see text]. We will characterize the space of newforms [Formula: see text] as the intersection of eigenspaces of a particular family of Hecke operators, generalizing previous work of Baruch–Purkait to forms with nontrivial character. We achieve this by obtaining representation theoretic results in the p-adic case which we then de-adelize into relations of classical Hecke operators.
Accelerated Bitrate Ladder Creation for Video Live Streaming Using Temporal Layer Injection
Authors: Mareen Hannes , Haems Casper , Wauters Tim , De Turck Filip , Lambert Peter , Wallendael Glenn Van
Published: July 2024

Video streaming systems aim to provide high-quality video adapted to clients’ device and network conditions. For this purpose, adaptive streaming architectures encode video content at a variety of quality levels, organized in a bitrate ladder. However, compressing a video into multiple streams is resource-intensive, which may become especially problematic in live streaming applications with real-time demands. Therefore, this paper proposes a novel solution for fast bitrate ladder creation, and provides the requirements for implementation in the H.266/VVC standard. More specifically, the proposed method creates new intermediate combined streams by injecting the lowest temporal layers of a higher-quality augmentation stream in a lower-quality base stream. Since the lowest layers are used as reference by the remaining layers, this procedure indirectly increases the quality of the frames in those untouched remaining layers as well. Although altering the reference frames may cause some drift-error artifacts, we demonstrate that, on average, the quality of the intermediate streams are higher than the base stream. Additionally, we demonstrate that injecting more layers brings both the quality and bitrate closer to that of the augmentation stream. The disadvantage of the combined streams is that their quality fluctuates more than the quality of the source streams, and that they are compressed less efficiently, comparable to going from a slower to fast or faster preset in the VVenC encoder. Most importantly, their main advantage is that they were generated at no significant additional computational complexity. In this way, the proposed method is of great benefit when generating a bitrate ladder of video streams under constrained computational resources. The code has been made open source and is available on https://github.com/IDLabMedia/NALUProcessing .
Optimization to release net zero emission in built environment from newly developed natural fiber–hybrid composite
Authors: Amuthan Tamilselvan , Nagaprasad Nagaraj , Somurajan Vijayan , Gupta Nakul , Kumar Ramakrishnan Kulasekaran Sathish , Krishnaraj Ramaswamy , Raj Vijilius Helena , Srinivas R. , Saxena Kuldeep K. , Deshmukh Sujata
Published: July 2024

This article focuses on investigating the dynamic behavior of hybrid composites involving natural fibers. The objective of this study is to examine and evaluate the effectiveness of recently used hybrid composite materials. The primary materials utilized are glass fiber-reinforced plastic. Plastic is applied to the surface of fiber-reinforced epoxy composites. Natural fibers are also used in other materials, polyester-based hybrid composites. The breakage pressure of the double shear test in the first composition has been improved from 1.10 to 1.38[Formula: see text]kN. The hardness rating was enhanced from 76.15 to 87.99. The interlamination experimental findings improved from 7.96 to 8.52[Formula: see text]kN. The results showed that a hybrid with a 50:50 comparable strand ratio of glass and sisal outscored a hybrid with a small fraction of sisal fiber. The second composite describes the effects of natural filler loading at 5%, 10%, and 15% on the mechanical behavior of a polyester-based hybrid. Materials created in a reputable environment for automotive and marine applications. Both hybrid nanocomposite preparation methods are tested, and the findings are validated. The electrical and mechanical characteristics of the materials are also validated and demonstrated to be superior to other current composites. This study highlights composite materials and technological advances and discusses natural fibers’ rich history and promise in many applications.
EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Authors: Zhang Yifan , Li Jiliang , Karas Zachary , Bansal Aakash , Li Toby Jia-Jun , McMillan Collin , Leach Kevin , Huang Yu
Published: July 2024

Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural representations like the Abstract Syntax Tree (AST), few studies have considered human attention — that is, where programmers focus while examining and comprehending code. In this paper, we develop a method for incorporating human attention into machine attention to enhance neural code summarization. To facilitate this incorporation and vindicate this hypothesis, we introduce EyeTrans, which consists of three steps: (1) we conduct an extensive eye-tracking human study to collect and pre-analyze data for model training, (2) we devise a data-centric approach to integrate human attention with machine attention in the Transformer architecture, and (3) we conduct comprehensive experiments on two code summarization tasks to demonstrate the effectiveness of incorporating human attention into Transformers. Integrating human attention leads to an improvement of up to 29.91% in Functional Summarization and up to 6.39% in General Code Summarization performance, demonstrating the substantial benefits of this combination. We further explore performance in terms of robustness and efficiency by creating challenging summarization scenarios in which EyeTrans exhibits interesting properties. We also visualize the attention map to depict the simplifying effect of machine attention in the Transformer by incorporating human attention. This work has the potential to propel AI research in software engineering by introducing more human-centered approaches and data.
IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion
Authors: Li Bolun , Sun Zhihong , Huang Tao , Zhang Hongyu , Wan Yao , Li Ge , Jin Zhi , Lyu Chen
Published: July 2024

Code completion aims to enhance programming productivity by predicting potential code based on the current

programming context. Recently, pre-trained language models (LMs) have become prominent in this field.

Various approaches have been proposed to fine-tune LMs using supervised fine-tuning (SFT) techniques for

code completion. However, the inherent exposure bias of these models can cause errors to accumulate early in

the sequence completion, leading to even more errors in subsequent completions. To address this problem,

deep reinforcement learning (DRL) is an alternative technique for fine-tuning LMs for code completion, which

can improve the generalization capabilities and overall performance. Nevertheless, integrating DRL-based

strategies into code completion faces two major challenges: 1) The dynamic nature of the code context requires

the completion model to quickly adapt to changes, which poses difficulties for conventional DRL strategies

that focus on delayed rewarding of the final code state. 2) It is difficult to evaluate the correctness of partial

code, thus the reward redistribution-based strategies cannot be adapted to code completion. To tackle these

challenges, we propose IRCoCo, a code completion-specific DRL-based fine-tuning framework. This framework

is designed to provide immediate rewards as feedback for detecting dynamic context changes arising from

continuous edits during code completion. With the aid of immediate feedback, the fine-tuned LM can gain

a more precise understanding of the current context, thereby enabling effective adjustment of the LM and

optimizing code completion in a more refined manner. Experimental results demonstrate that fine-tuning pre-trained LMs with IRCoCo leads to significant improvements in the code completion task, outperforming

both SFT-based and other DRL-based baselines.
Maximizing Patch Coverage for Testing of Highly-Configurable Software without Exploding Build Times
Authors: Yıldıran Necip Fazıl , Oh Jeho , Lawall Julia , Gazzillo Paul
Published: July 2024

The Linux kernel is highly-configurable, with a build system that takes a configuration file as input and automatically tailors the source code accordingly. Configurability, however, complicates testing, because different configuration options lead to the inclusion of different code fragments. With thousands of patches received per month, Linux kernel maintainers employ extensive automated continuous integration testing. To attempt patch coverage, i.e., taking all changed lines into account, current approaches either use configuration files that maximize total statement coverage or use multiple randomly-generated configuration files, both of which incur high build times without guaranteeing patch coverage. To achieve patch coverage without exploding build times, we propose krepair, which automatically repairs configuration files that are fast-building but have poor patch coverage to achieve high patch coverage with little effect on build times. krepair works by discovering a small set of changes to a configuration file that will ensure patch coverage, preserving most of the original configuration file's settings. Our evaluation shows that, when applied to configuration files with poor patch coverage on a statistically-significant sample of recent Linux kernel patches, krepair achieves nearly complete patch coverage, 98.5% on average, while changing less than 1.53% of the original default configuration file in 99% of patches, which keeps build times 10.5x faster than maximal configuration files.
Understanding Developers’ Discussions and Perceptions on Non-functional Requirements: The Case of the Spring Ecosystem
Authors: Oliveira Anderson , Correia João , Assunção Wesley K. G. , Pereira Juliana Alves , de Mello Rafael , Coutinho Daniel , Barbosa Caio , Libório Paulo , Garcia Alessandro
Published: July 2024

Non-Functional Requirements (NFRs) should be defined in the early stages of the software development process, driving developers to make important design decisions. Neglecting NFRs may lead developers to create systems that are difficult to maintain and do not meet users expectations. Despite their importance, the discussion of NFRs is often ad-hoc and scattered through multiple sources, limiting developers' awareness of NFRs. In that scenario, Pull Request (PR) discussions provide a centralized platform for comprehensive NFR discussions. However, existing studies do not explore this important source of information in open-source software development, which developers widely use to discuss software requirements. In this study, we report an investigation of NFR discussions in PRs of repositories of the Spring ecosystem. We collected, manually curated, and analyzed PR discussions addressing four categories of NFRs: maintainability, security, performance, and robustness. We observed that discussions surrounding these PRs tend to address the introduction of a code change or explain some anomaly regarding a particular NFR. Also, we found that more than 77% of the discussions related to NFRs are triggered in the PR title and/or description, indicating that developers are often provided with information regarding NFRs straightway. To gain additional knowledge from these NFR discussions, our study also analyzed the characteristics and activities of developers who actually discuss and fix NFR issues. In particular, we performed an in-depth analysis of 63 developers that stood out in collaborating with the mapped PRs. To complement this analysis, we conducted a survey with 44 developers to gather their perceptions on NFR discussions. By observing how developers approach NFRs and participate in discussions, we documented the best practices and strategies newcomers can adopt to address NFRs effectively. We also provided a curated dataset of 1,533 PR discussions classified with NFR presence.
Rocks Coding, Not Development: A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks
Authors: Wang Wei , Ning Huilong , Zhang Gaowei , Liu Libo , Wang Yi
Published: July 2024

Recently, large language models (LLM) based generative AI has been gaining momentum for their impressive high-quality performances in multiple domains, particularly after the release of the ChatGPT. Many believe that they have the potential to perform general-purpose problem-solving in software development and replace human software developers. Nevertheless, there are in a lack of serious investigation into the capability of these LLM techniques in fulfilling software development tasks. In a controlled 2 × 2 between-subject experiment with 109 participants, we examined whether and to what degree working with ChatGPT was helpful in the coding task and typical software development task and how people work with ChatGPT. We found that while ChatGPT performed well in solving simple coding problems, its performance in supporting typical software development tasks was not that good. We also observed the interactions between participants and ChatGPT and found the relations between the interactions and the outcomes. Our study thus provides first-hand insights into using ChatGPT to fulfill software engineering tasks with real-world developers and motivates the need for novel interaction mechanisms that help developers effectively work with large language models to achieve desired outcomes.
Only diff Is Not Enough: Generating Commit Messages Leveraging Reasoning and Action of Large Language Model
Authors: Li Jiawei , Faragó David , Petrov Christian , Ahmed Iftekhar
Published: July 2024

Commit messages play a vital role in software development and maintenance. While previous research has introduced various Commit Message Generation (CMG) approaches, they often suffer from a lack of consideration for the broader software context associated with code changes. This limitation resulted in generated commit messages that contained insufficient information and were poorly readable. To address these shortcomings, we approached CMG as a knowledge-intensive reasoning task. We employed ReAct prompting with a cutting-edge Large Language Model (LLM) to generate high-quality commit messages. Our tool retrieves a wide range of software context information, enabling the LLM to create commit messages that are factually grounded and comprehensive. Additionally, we gathered commit message quality expectations from software practitioners, incorporating them into our approach to further enhance message quality. Human evaluation demonstrates the overall effectiveness of our CMG approach, which we named Omniscient Message Generator (OMG). It achieved an average improvement of 30.2% over human-written messages and a 71.6% improvement over state-of-the-art CMG methods.
DeSQL: Interactive Debugging of SQL in Data-Intensive Scalable Computing
Authors: Haroon Sabaat , Brown Chris , Gulzar Muhammad Ali
Published: July 2024

SQL is the most commonly used front-end language for data-intensive scalable computing (DISC) applications due to its broad presence in new and legacy workflows and shallow learning curve. However, DISC-backed SQL introduces several layers of abstraction that significantly reduce the visibility and transparency of workflows, making it challenging for developers to find and fix errors in a query. When a query returns incorrect outputs, it takes a non-trivial effort to comprehend every stage of the query execution and find the root cause among the input data and complex SQL query. We aim to bring the benefits of step-through interactive debugging to DISC-powered SQL with DeSQL. Due to the declarative nature of SQL, there are no ordered atomic statements to place a breakpoint to monitor the flow of data. DeSQL’s automated query decomposition breaks a SQL query into its constituent sub queries, offering natural locations for setting breakpoints and monitoring intermediate data. However, due to advanced query optimization and translation in DISC systems, a user query rarely matches the physical execution, making it challenging to associate subqueries with their intermediate data. DeSQL performs fine-grained taint analysis to dynamically map the subqueries to their intermediate data, while also recognizing subqueries removed by the optimizers. For such subqueries, DeSQL efficiently regenerates the intermediate data from a nearby subquery’s data. On the popular TPC-DC benchmark, DeSQL provides a complete debugging view in 13% less time than the original job time while incurring an average overhead of 10% in addition to retaining Apache Spark’s scalability. In a user study comprising 15 participants engaged in two debugging tasks, we find that participants utilizing DeSQL identify the root cause behind a wrong query output in 74% less time than the de-facto, manual debugging.
SimLLM: Calculating Semantic Similarity in Code Summaries using a Large Language Model-Based Approach
Authors: Jin Xin , Lin Zhiqiang
Published: July 2024

Code summaries are pivotal in software engineering, serving to improve code readability, maintainability, and collaboration. While recent advancements in Large Language Models (LLMs) have opened new avenues for automatic code summarization, existing metrics for evaluating summary quality, such as BLEU and BERTScore, have notable limitations. Specifically, these existing metrics either fail to capture the nuances of semantic meaning in summaries or are further limited in understanding domain-specific terminologies and expressions prevalent in code summaries. In this paper, we present SimLLM, a novel LLM-based approach designed to more precisely evaluate the semantic similarity of code summaries. Built upon an autoregressive LLM using a specialized pretraining task on permutated inputs and a pooling-based pairwise similarity measure, SimLLM overcomes the shortcomings of existing metrics. Our empirical evaluations demonstrate that SimLLM not only outperforms existing metrics but also shows a significantly high correlation with human ratings.
Towards Efficient Build Ordering for Incremental Builds with Multiple Configurations
Authors: Lyu Jun , Li Shanshan , Zhang He , Yang Lanxin , Liu Bohan , Rigger Manuel
Published: July 2024

Software products have many configurations to meet different environments and diverse needs. Building software with multiple software configurations typically incurs high costs in terms of build time and computing resources. Incremental builds could reuse intermediate artifacts if configuration settings affect only a portion of the build artifacts. The efficiency gains depend on the strategic ordering of the incremental builds as the order influences which build artifacts can be reused. Deriving an efficient order is challenging and an open problem, since it is infeasible to reliably determine the degree of re-use and time savings before an actual build. In this paper, we propose an approach, called BUDDI—BUild Declaration DIstance, for C-based and Make-based projects to derive an efficient order for incremental builds from the static information provided by the build scripts (i.e., Makefile). The core strategy of BUDDI is to measure the distance between the build declarations of configurations and predict the build size of a configuration from the build targets and build commands in each configuration. Since some artifacts could be reused in the subsequent builds if there is a close distance between the build scripts for different configurations. We implemented BUDDI as an automated tool called BuddiPlanner and evaluated it on 20 popular open-source projects, by comparing it to a baseline that randomly selects a build order. The experimental results show that the order created by BuddiPlanner outperforms 96.5% (193/200) of the random build orders in terms of build time and reduces the build time by an average of 305.94s (26%) compared to the random build orders, with a median saving of 64.88s (28%). BuddiPlanner demonstrates its potential to relieve practitioners of excessive build times and computational resource burdens caused by building multiple software configurations.
Cut to the Chase: An Error-Oriented Approach to Detect Error-Handling Bugs
Authors: Liu Haoran , Jia Zhouyang , Li Shanshan , Lei Yan , Yu Yue , Jiang Yu , Mao Xiaoguang , Liao Xiangke
Published: July 2024

Error-handling bugs are prevalent in software systems and can result in severe consequences. Existing works on error-handling bug detection can be categorized into template-based and learning-based approaches. The former requires much human effort and is difficult to accommodate the software evolution. The latter usually focuses on errors of API and assumes that error handling should be right after the handled error. Such an assumption, however, may affect both learning and detecting phases.

The existing learning-based approaches can be regarded as API-oriented, which starts from an API and learns if the API requires error handling. In this paper, we propose EH-Digger, an ERROR-oriented approach, which starts from an error handling. Our approach can learn why the error occurs and when the error has to be handled. We conduct a comprehensive study on 2,322 error-handling code snippets from 22 widely used software systems across 8 software domains to reveal the limitation of existing approaches and guide the design of EH-Digger. We evaluated EH-Digger on the Linux Kernel and 11 open-source applications. It detected 53 new bugs confirmed by the developers and 71 historical bugs fixed in the latest versions. We also compared EH-Digger with three state-of-the-art approaches, 30.1% of bugs detected by EH-Digger cannot be detected by the existing approaches.