Enhancing efficiency, correctness, and social fairness in automated code generation

Large Language Models (LLMs) are increasingly integrated into IDEs to assist with software development tasks such as code generation, debugging, and testing. LLMs have significantly enhanced developer productivity by generating code from natural language instructions. However, despite these advancements, LLM-generated code often suffers from critical shortcomings: functional incorrectness, poor efficiency, and social biases. These limitations hinder the practical deployment of LLMs in real-world software engineering, particularly in performance-critical and socially sensitive contexts. Functional incorrectness in LLM-generated code requires extensive manual intervention to debug and repair, slowing down software development workflows. Poor efficiency leads to increased execution time and resource consumption, rendering the code impractical for use in resource-constrained environments such as embedded systems or mobile devices. Inefficiency also exacerbates energy consumption, a growing concern for sustainable software engineering. Meanwhile, biases embedded in LLM-generated code can perpetuate inequities in critical applications, such as hiring algorithms or healthcare systems, limiting societal applicability. Addressing these challenges is essential to unlock the full potential of LLMs in software development. This thesis proposes a comprehensive framework to address these challenges, presenting three key contributions that focus on improving the efficiency, correctness, and social fairness of LLM-generated code. First, we propose EffiBench and EffiLearner to address the inefficiency of LLM-generated code. EffiBench introduces the first benchmark specifically designed to measure efficiency, incorporating a collection of 1,000 efficiency-critical problems paired with canonical solutions optimized for time and space complexity. It integrates comprehensive test cases and diverse metrics, such as execution time and memory usage, to evaluate the efficiency of LLM-generated code. Building on this foundation, EffiLearner leverages the insights from EffiBench to introduce a self-optimization framework inspired by human coding practices. EffiLearner refines LLM-generated code iteratively using execution profiles that reveal computational overheads, enabling LLMs to reduce execution time and memory usage while improving overall efficiency. Second, to simultaneously improve correctness and efficiency, we introduce EffiCoder, a fine-tuning dataset and framework that extends existing efforts. EffiCoder aggregates optimized solutions from multiple datasets and generates rich metadata and test cases to evaluate execution performance. By incorporating iterative self-optimization into the dataset construction process, EffiCoder enables LLMs to produce correct and high-performing code that balances functional requirements and computational efficiency. This framework bridges the gap left by previous fine-tuning approaches, which often focused exclusively on correctness. Finally, to address social fairness, we propose the Code Bias Score (CBS) framework for evaluating and mitigating biases in LLM-generated code for bias-sensitive tasks. CBS employs automated test generation and Abstract Syntax Tree analysis to detect and quantify bias behaviors in generated code. In addition to evaluating fairness, CBS provides feedback to LLMs, guiding them to reduce biases during code generation. This approach ensures that LLMs produce code that adheres to ethical and equitable standards without sacrificing performance. These contributions provide a unified framework for addressing the core limitations of LLM-generated code. By ensuring efficiency, correctness, and social fairness, this thesis paves the way for the broader adoption of LLMs in real-world software engineering, fostering sustainable, reliable, and socially responsible practices.published_or_final_versionComputer ScienceDoctoralDoctor of Philosoph

Multifunctional Universal Additive for Stable and Efficient Inverted Perovskite Solar Cells

The performance of perovskite solar cells has significantly improved over the years in part due to defect passivation in the bulk and at the interfaces. While many additive molecules have been reported in the literature, they are commonly applicable only to one particular perovskite composition. Here we investigate a multifunctional additive, 4-amino-5-bromo nicotinic acid (ABrNA), for use in both methylammonium (MA)-free perovskites with different Br content (bandgaps ranging from 1.53 to 1.73 eV) as well as MA-containing perovskites. Significant performance improvements are obtained for all compositions, which can be attributed to the presence of multiple functional groups capable of modifying the crystallization of the perovskite as well as passivating defects. Exceptional features of ABrNA make it a promising universal passivator, which leads to a PCE increase from 23.9% to 25.0% for CsFAMA solar cells, and from 22.0% to 23.0% for MA-free solar cells. The ABrNA passivated MA-free devices also exhibit exceptional operational stability, with T90 exceeding 1000 h under ISOS-L-1 testing conditions. In addition, significant performance improvement is observed with ABrNA for modules in both conventional and inverted device architectures, further confirming the universality of ABrNA additive

New washing and sieving method for separation and evaluation of soil particles to nano-size

Soil particles comprise nano-sized clay particles, micro-sized silt particles, milli-sized sand particles, and centi-sized gravel particles. The proposed new washing and sieving method extends the existing standard wet sieving method and uses the nylon filter cloth to accurately separate the silt and clay mixture into different size groups of particles to nano-sizes. Through a large amount of particle images and experimental data, these separated particles from general soils can be an important source of new productive forces. The first part (Chapters 2 to 3) proposes a new washing and sieving method. This method extends the standard wet sieving method using steel sieves with aperture sizes ≥0.063 mm (or ≥0.075 mm) for gravel and sand particles separation and using nylon sieves with aperture sizes from 48 μm, 38 μm, 14 μm, 12 μm, 6.3 μm, 4 μm, 3 μm, 2 μm, 1 μm, 800 nm, 500 nm, 400 nm, 200 nm, to 100 nm, respectively, for silt and clay particles separation. The availability of nano-scale particles helps determine the cut-off size between clay mineral particles and silt particles, and study the properties of nano-clays in geotechnical applications. Both manual-based and machine-based methods are universal for particle separation with high accuracy. The stereomicroscopic and SEM images checking for individual particles verify the correctness of particle sizes. The proposed new washing and sieving method is conceptually straightforward, operationally simple, and material available. The second part (Chapters 4 to 7) establishes a refined approach to evaluate the geometrical, physical, chemical, and mineralogical properties of soil particles. Part I applies the proposed washing and sieving method to separate general soil into many sub-groups of particles with known size ranges. Part II uses standard laboratory test techniques to assess the properties of individual particles. These techniques include dynamic image analysis particle measurement, stereomicroscopy, scanning electron microscopy (SEM), Atterberg limits test, permeability test, energy-dispersive X-ray spectroscopy (EDS), X-ray fluorescence (XRF), and X-ray diffraction (XRD) tests. Based on the test results, the relationship between particle properties and particle size, as well as the relationship between the bulk behavior of soil and the properties of its constituent particles are accurately quantified. A specific type of soil such as completely decomposed granitic (CDG) or volcanic soil (CDV), marine deposits, and expansive soil can be quantitatively studied based on the properties of individual particles. The third part (Chapter 8) expands the proposed washing and sieving method for the basic soil classification of the other 45 types of soils from 37 sites throughout China. These soils are mainly collected from paddy lands, dry farmlands, forests, woods, natural hillside slopes, and public fill banks. Their particle size distributions, some physical properties, and refined soil names are briefly described in this part. The test results show that the new washing and sieving method is applicable to the separation and classification of many different types of soils from various sites, which helps establish the particle and mineral sequence map of soils on the ground and provides particle materials for further studies.published_or_final_versionCivil EngineeringDoctoralDoctor of Philosoph

Low tech-barrier virtual reality content creation for cultural heritage education: learning outcomes and pedagogy

Aligned with the United Nation’s Sustainable Development Goals, virtual reality (VR) content creation helps novice learners better understand about cultural heritage, yet its pedagogical potential remains under-explored. Synthesising the literature, this study developed a multidimensional framework of learning outcomes in the interdisciplinary domain of cultural heritage education. Based on the principles of designing maker activities, this study designed a novel, research-informed and low tech-barrier pedagogical approach of VR content creation. Applying it to a general education course on digitising cultural heritage, the authors analysed instructor-given, rubric-referenced performance scores and qualitatively examined reflections from 302 undergraduate students of diverse disciplinary backgrounds. Results have shown that the maker activity of VR content creation outperformed other traditional learning approaches in terms of students’ learning outcome achievement. Based on empirical evidence, this study verified the learning outcome framework, yielded the corresponding pedagogical implications and laid the groundwork for future studies on VR-based maker activities.</p

Attitudinal Shifts towards Homosexuality among Secondary School Students in Hong Kong over a Decade: A Multiple Group Latent Class Analysis

Purpose: This study aims to investigate the heterogeneity in attitudes toward homosexuality among secondary school students in Hong Kong and examine whether and how the attitude patterns had shifted between 2011 and 2021. Factors that influenced the attitude patterns were also examined. Methods: Three dimensions of attitudes toward homosexuality were measured: general attitudes, attitudes toward formal rights, and attitudes toward informal privileges. Latent class analysis and multiple group latent class analysis were employed to analyze data from a repeated cross-sectional survey conducted in 2011, 2016, and 2021, involving a total of 10,769 adolescents. Results: In 2011, three attitude classes were identified: intolerant, neutral, and inclusive. However, both 2016 and 2021 revealed a four-class model, consisting of three classes similar to those observed in 2011, and a new “partially inclusive” class. The prevalence of these attitude classes had shifted over the study period. Sex, age, sexual orientation, and exposure to online sexual knowledge were associated with the attitude patterns. Conclusion: A notable increase in the acceptance of homosexuality was observed among Hong Kong adolescents from 2011 to 2021. However, their attitudes toward different topics of homosexuality were not entirely consistent. Policy implications: This study provides valuable insights for policymakers and practitioners in assessing the compatibility of policies and practices with evolving attitudes. It underscores the importance of addressing not only the formal rights of sexual minority groups but also the more subtle, yet significant forms of discrimination they may face

C1q+ Macrophage–Tumor Cell Interaction Promoted Tumorigenesis via GPR17/PI3K/AKT Pathway Induced DNA Hypermethylation in Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is one of the common head and neck cancers in Southern China and Southeast Asia. Although current studies have adequately characterized the tumor microenvironment (TME) of NPC, little attention has been paid to how cell-cell interactions within the TME promote tumorigenesis. In this study, it is found that C1q+ tumor-associated macrophages (TAMs) are significantly enriched in NPC tumors. Moreover, both enriched C1q+ TAMs and elevated C1q expression are associated with the progression and poor prognosis in NPC patients. In vitro and in vivo studies demonstrate that C1q directly boosts the malignancy and stemness of tumor cells. Mechanistically, C1q activates the Phosphatidylinositol-3-kinase (PI3K)/AKT pathway through interacting with GPR17, a member of the G protein-coupled receptor family, thereby inducing DNA hypermethylation of tumor cells to promote tumor development. It is further proved that DNA hypermethylated NPC cells induced by C1q elicited the immunosuppressive phenotype of TAMs. Targeted blockade of C1q with a neutralizing antibody restricts NPC progression in the humanized mouse model. It is assumed that the differentiation of C1q+ TAMs possibly acquired both M1 and M2 polarization conditions. These findings provide new insights into the cellular communication in the TME of NPC and may have important applications for the development of new targeted therapies.published_or_final_versio

Enhancement of the thermoelectric performance of SnTe via Mn solubility control

Thermoelectric materials have drawn attention due to their capability of directly converting heat and electricity, which helps utilize waste heat and provides an alternative energy source. SnTe is a competitive candidate for thermoelectric performance at medium-high temperature ranges and has become a promising lead-free thermoelectric material. However, the high carrier concentration and thermal conductivity limit its thermoelectric performance and lead to different strategies to enhance its dimensionless figure of merit (zT). Herein, we report an improvement in the thermoelectric performance of SnTe via Ge, Mn, and AgBiSe2 co-alloying. The introduction of AgBiSe2 preliminarily reduces electrical and thermal conductivity, while the co-alloying of Ge and Mn significantly increases the Seebeck coefficient at room temperature and reduces the lattice thermal conductivity. The Sn0.73Ge0.1Mn0.2Te + 3% AgBiSe2 sample exhibits the highest zT of ∼1.44 at 823 K and an average zT of ∼0.71 between 300 and 823 K

Yeast-two-hybrid based high-throughput screening to discover SARS-CoV-2 fusion inhibitors by targeting the HR1/HR2 interaction

The continuous emergence of SARS-CoV-2 variants as well as other potential future coronavirus has challenged the effectiveness of current COVID-19 vaccines. Therefore, there remains a need for alternative antivirals that target processes less susceptible to mutations, such as the formation of six-helix bundle (6-HB) during the viral fusion step of host cell entry. In this study, a novel high-throughput screening (HTS) assay employing a yeast-two-hybrid (Y2H) system was established to identify inhibitors of HR1/HR2 interaction. The compound IMB-9C, which achieved single-digit micromolar inhibition of SARS-CoV-2 and its Omicron variants with low cytotoxicity, was selected. IMB-9C effectively blocks the HR1/HR2 interaction in vitro and inhibits SARS-CoV-2-S-mediated cell–cell fusion. It binds to both HR1 and HR2 through non-covalent interaction and influences the secondary structure of HR1/HR2 complex. In addition, virtual docking and site-mutagenesis results suggest that amino acid residues A930, I931, K933, T941, and L945 are critical for IMB-9C binding to HR1. Collectively, in this study, we have developed a novel screening method for HR1/HR2 interaction inhibitors and identified IMB-9C as a potential antiviral small molecule against COVID-19 and its variants.published_or_final_versio