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Articles 1561 - 1590 of 8681
Full-Text Articles in Entire DC Network
Colefunda: Explainable Silent Vulnerability Fix Identification, Jiayuan Zhou, Michael Pacheco, Jinfu Chen, Xing Hu, Xin Xia, David Lo, Ahmed E. Hassan
Colefunda: Explainable Silent Vulnerability Fix Identification, Jiayuan Zhou, Michael Pacheco, Jinfu Chen, Xing Hu, Xin Xia, David Lo, Ahmed E. Hassan
Research Collection School Of Computing and Information Systems
It is common practice for OSS users to leverage and monitor security advisories to discover newly disclosed OSS vulnerabilities and their corresponding patches for vulnerability remediation. It is common for vulnerability fixes to be publicly available one week earlier than their disclosure. This gap in time provides an opportunity for attackers to exploit the vulnerability. Hence, OSS users need to sense the fix as early as possible so that the vulnerability can be remediated before it is exploited. However, it is common for OSS to adopt a vulnerability disclosure policy which causes the majority of vulnerabilities to be fixed silently, …
Generation-Based Code Review Automation: How Far Are We?, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, Junda He, David Lo
Generation-Based Code Review Automation: How Far Are We?, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, Junda He, David Lo
Research Collection School Of Computing and Information Systems
Code review is an effective software quality assurance activity; however, it is labor-intensive and time-consuming. Thus, a number of generation-based automatic code review (ACR) approaches have been proposed recently, which leverage deep learning techniques to automate various activities in the code review process (e.g., code revision generation and review comment generation).We find the previous works carry three main limitations. First, the ACR approaches have been shown to be beneficial in each work, but those methods are not comprehensively compared with each other to show their superiority over their peer ACR approaches. Second, general-purpose pre-trained models such as CodeT5 are proven …
Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Research Collection School Of Computing and Information Systems
Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed …
Liloc: Enabling Precise 3d Localization In Dynamic Indoor Environments Using Lidars, Darshana Rathnayake, Meera Radhakrishnan, Inseok Hwang, Archan Misra
Liloc: Enabling Precise 3d Localization In Dynamic Indoor Environments Using Lidars, Darshana Rathnayake, Meera Radhakrishnan, Inseok Hwang, Archan Misra
Research Collection School Of Computing and Information Systems
We present LiLoc, a system for precise 3D localization and tracking of mobile IoT devices (e.g., robots) in indoor environments using multi-perspective LiDAR sensing. The key differentiators in our work are: (a) First, unlike traditional localization approaches, our approach is robust to dynamically changing environmental conditions (e.g., varying crowd levels, object placement/layout changes); (b) Second, unlike prior work on visual and 3D SLAM, LiLoc is not dependent on a pre-built static map of the environment and instead works by utilizing dynamically updated point clouds captured from both infrastructural-mounted LiDARs and LiDARs equipped on individual mobile IoT devices. To achieve fine-grained, …
Monoracials’ Perceptions Of Biracials In Singapore, Sheila Xi Rui Wee, Chi-Ying Cheng
Monoracials’ Perceptions Of Biracials In Singapore, Sheila Xi Rui Wee, Chi-Ying Cheng
Research Collection School of Social Sciences
Objectives: Drawing on social identity theory, present research examined the effects of overlapping racial membership on monoracials’ categorization of biracials as in-/out-group members, as well as its impact on monoracials’ social perceptions of biracials in Singapore. Within Singapore, it is hypothesized that biracials who share racial membership with monoracials would be rated more as monoracials’ racial in-group and be evaluated more positively. Furthermore, monoracials’ positive perceptions of biracials with (vs. without) shared racial membership would be less influenced by biracials’ confrontation of racial prejudice. Method: Studies 1 (N = 242) and 2 (N = 153) sampled Chinese Singaporeans to assess …
Overcoming Challenges In Devops Education Through Teaching Methods, Samuel Ferino, Marcelo Fernandes, Elder Cirilo, Lucas Agnez, Bruno Batista, Uirá Kulesza, Eduardo Aranha, Christoph Treude
Overcoming Challenges In Devops Education Through Teaching Methods, Samuel Ferino, Marcelo Fernandes, Elder Cirilo, Lucas Agnez, Bruno Batista, Uirá Kulesza, Eduardo Aranha, Christoph Treude
Research Collection School Of Computing and Information Systems
DevOps is a set of practices that deals with coordination between development and operation teams and ensures rapid and reliable new software releases that are essential in industry. DevOps education assumes the vital task of preparing new professionals in these practices using appropriate teaching methods. However, there are insufficient studies investigating teaching methods in DevOps. We performed an analysis based on interviews to identify teaching methods and their relationship with DevOps educational challenges. Our findings show that project-based learning and collaborative learning are emerging as the most relevant teaching methods.
Navigating Complexity In Software Engineering: A Prototype For Comparing Gpt-N Solutions, Christoph Treude
Navigating Complexity In Software Engineering: A Prototype For Comparing Gpt-N Solutions, Christoph Treude
Research Collection School Of Computing and Information Systems
Navigating the diverse solution spaces of non-trivial software engineering tasks requires a combination of technical knowledge, problem-solving skills, and creativity. With multiple possible solutions available, each with its own set of trade-offs, it is essential for programmers to evaluate the various options and select the one that best suits the specific requirements and constraints of a project. Whether it is choosing from a range of libraries, weighing the pros and cons of different architecture and design solutions, or finding unique ways to fulfill user requirements, the ability to think creatively is crucial for making informed decisions that will result in …
Win: Weight-Decay-Integrated Nesterov Acceleration For Adaptive Gradient Algorithms, Pan Zhou, Xingyu Xie, Shuicheng Yan
Win: Weight-Decay-Integrated Nesterov Acceleration For Adaptive Gradient Algorithms, Pan Zhou, Xingyu Xie, Shuicheng Yan
Research Collection School Of Computing and Information Systems
Training deep networks on large-scale datasets is computationally challenging. In this work, we explore the problem of “how to accelerate adaptive gradient algorithms in a general manner”, and aim to provide practical efficiency-boosting insights. To this end, we propose an effective and general Weight-decay-Integrated Nesterov acceleration (Win) to accelerate adaptive algorithms. Taking AdamW and Adam as examples, we minimize a dynamical loss per iteration which combines the vanilla training loss and a dynamic regularizer inspired by proximal point method (PPM) to improve the convexity of the problem. To introduce Nesterov-alike-acceleration into AdamW and Adam, we respectively use the first- and …
Bilingual Profiles Differentially Predict Executive Functions During Early Childhood: A Latent Profile Analysis, Hwajin Yang, Germaine Y. Q. Tng, Wee Qin Ng, Sujin Yang
Bilingual Profiles Differentially Predict Executive Functions During Early Childhood: A Latent Profile Analysis, Hwajin Yang, Germaine Y. Q. Tng, Wee Qin Ng, Sujin Yang
Research Collection School of Social Sciences
Recent studies suggest that heterogeneous bilingual experiences implicate different executive functions (EF) in children. Using a latent profile analysis, we conducted a more nuanced investigation of multifaceted bilingual experiences. By concurrently considering numerous bilingual indicators – age of L1 and L2 acquisition, interactional contexts of verbal exchanges, L1 and L2 proficiency, balance of language use at home and school, and receptive vocabulary – we identified three latent profiles (subgroups): balanced dual-language, dominant single-language, and mixed-interaction. We found that the balanced dual-language and dominant single-language profiles predicted significantly better switching than the mixed-interaction profile. However, no profile differences were found in …
The Sec Revolving Door And Comment Letters, Michael Shen, Samuel T. Tan
The Sec Revolving Door And Comment Letters, Michael Shen, Samuel T. Tan
Research Collection School Of Accountancy
Government officials, advocacy groups, and the business press have raised concerns that former SEC employees may continue to influence the SEC after leaving the agency. Using hand-collected data on the characteristics of 1,384 lawyers who represented firms in responding to SEC comment letters between 2005 and 2016, we examine the impact of post-revolving SEC employees on the SEC comment letter process. Among other determinants, we find that older and larger firms with a history of litigation are more likely to hire former SEC lawyers over non-SEC lawyers. Relative to firms that involve only non-SEC lawyers, we find that firms that …
Towards Understanding Why Mask Reconstruction Pretraining Helps In Downstream Tasks, Jiachun Pan, Pan Zhou, Shuicheng Yan
Towards Understanding Why Mask Reconstruction Pretraining Helps In Downstream Tasks, Jiachun Pan, Pan Zhou, Shuicheng Yan
Research Collection School Of Computing and Information Systems
For unsupervised pretraining, mask-reconstruction pretraining (MRP) approaches, e.g. MAE (He et al., 2021) and data2vec (Baevski et al., 2022), randomly mask input patches and then reconstruct the pixels or semantic features of these masked patches via an auto-encoder. Then for a downstream task, supervised fine-tuning the pretrained encoder remarkably surpasses the conventional “supervised learning" (SL) trained from scratch. However, it is still unclear 1) how MRP performs semantic feature learning in the pretraining phase and 2) why it helps in downstream tasks. To solve these problems, we first theoretically show that on an auto-encoder of a two/one-layered convolution encoder/decoder, MRP …
Contrabert: Enhancing Code Pre-Trained Models Via Contrastive Learning, Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, Yang. Liu
Contrabert: Enhancing Code Pre-Trained Models Via Contrastive Learning, Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, Yang. Liu
Research Collection School Of Computing and Information Systems
Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code representation, they have been further applied in multiple downstream tasks such as clone detection, code search and code translation. However, it is also observed that these state-of-the-art pre-trained models are susceptible to adversarial attacks. The performance of these pre-trained models drops significantly with simple perturbations such as renaming variable names. This weakness may be inherited by their downstream models and thereby amplified at an unprecedented scale. To this end, we propose an approach namely ContraBERT that aims …
Graph Neural Point Process For Temporal Interaction Prediction, Wenwen Xia, Yuchen Li, Shengdong Li
Graph Neural Point Process For Temporal Interaction Prediction, Wenwen Xia, Yuchen Li, Shengdong Li
Research Collection School Of Computing and Information Systems
Temporal graphs are ubiquitous data structures in many scenarios, including social networks, user-item interaction networks, etc. In this paper, we focus on predicting the exact time of the next interaction, given a node pair on a temporal graph. This novel problem can support interesting applications, such as time-sensitive items recommendation, congestion prediction on road networks, and many others. We present Graph Neural Point Process (GNPP) to tackle this problem. GNPP relies on the graph neural message passing and the temporal point process framework. Most previous graph neural models only utilize the chronological order of observed events and ignore exact timestamps. …
No Destination: Queering Mobility Through The Virtuality Of Movement, Orlando Woods
No Destination: Queering Mobility Through The Virtuality Of Movement, Orlando Woods
Research Collection College of Integrative Studies
This paper advances the epistemological potential that exists at the nexus of queer theory and mobilities research. It aims to queer mobility by rejecting the idea of the destination and embracing the virtuality of movement instead. In doing so, it draws on the queer symbolism of the closet and the cruise to highlight the heteronormative framing that has come to define and constrain the new mobilities paradigm. Arguing that anybody has the capacity to be “queer”, it calls for a redefinition of the subject and an exploration of the world-making possibilities that emerge when the virtuality of movement is foregrounded.
To Ask Better Questions, Teach: Learning-By-Teaching Enhances Research Question Generation More Than Retrieval Practice And Concept-Mapping, Sarah Shi Hui Wong, Kagen Y. L. Lim, Stephen Wee Hun Lim
To Ask Better Questions, Teach: Learning-By-Teaching Enhances Research Question Generation More Than Retrieval Practice And Concept-Mapping, Sarah Shi Hui Wong, Kagen Y. L. Lim, Stephen Wee Hun Lim
Research Collection School of Social Sciences
Asking good questions is vital for scientific learning and discovery, but improving this complex skill is a formidable challenge. Here, we show in two experiments (N = 152) that teaching others—learning-by-teaching—enhances one’s ability to generate higher-order research questions that create new knowledge, relative to two other well-established generative learning techniques: retrieval practice and concept-mapping. Learners who taught scientific expository texts across natural and social sciences topics by delivering video-recorded lectures outperformed their peers who practiced retrieval or constructed concept maps when tested on their ability to generate create-level research questions based on the texts (Experiment 1). This advantage held reliably …
“Back To The Jungle”: Investigating Rats, Grass, Scrub Typhus, And Plantations In Malaya, 1924–1974, Jack Greatrex
“Back To The Jungle”: Investigating Rats, Grass, Scrub Typhus, And Plantations In Malaya, 1924–1974, Jack Greatrex
Research Collection College of Integrative Studies
In this article, I examine epidemiological research into scrub typhus in British Malaya between 1924 and 1974. Interwar research, I show, explained the incidence of the disease through conjunctions of rats, mites, plantations, lalang grass, and “jungle.” In the process, interwar researchers bridged a novel scientific vocabulary centering on disease “reservoirs” with older suspicions of plantations enabling “pests,” as well as with a later, explicitly ecological understanding of infectious disease. In exploring this history I thereby help to re-historicize the emergence of ecological notions of disease reservoirs, whilst also pushing at the limit-points of influential notions of “tropicality.”
Ncq: Code Reuse Support For Node.Js Developers, Brittany Reid, Marcelo D'Amorim, Markus Wagner, Christoph Treude
Ncq: Code Reuse Support For Node.Js Developers, Brittany Reid, Marcelo D'Amorim, Markus Wagner, Christoph Treude
Research Collection School Of Computing and Information Systems
Code reuse is an important part of software development. The adoption of code reuse practices is especially common among Node.js developers. The Node.js package manager, NPM, indexes over 1 Million packages and developers often seek out packages to solve programming tasks. Due to the vast number of packages, selecting the right package is difficult and time consuming. With the goal of improving productivity of developers that heavily reuse code through third-party packages, we present Node Code Query (NCQ), a Read-Eval-Print-Loop environment that allows developers to 1) search for NPM packages using natural language queries, 2) search for code snippets related …
Fine-Grained Commit-Level Vulnerability Type Prediction By Cwe Tree Structure, Shengyi Pan, Lingfeng Bao, Xin Xia, David Lo, Shanping Li
Fine-Grained Commit-Level Vulnerability Type Prediction By Cwe Tree Structure, Shengyi Pan, Lingfeng Bao, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Identifying security patches via code commits to allow early warnings and timely fixes for Open Source Software (OSS) has received increasing attention. However, the existing detection methods can only identify the presence of a patch (i.e., a binary classification) but fail to pinpoint the vulnerability type. In this work, we take the first step to categorize the security patches into fine-grained vulnerability types. Specifically, we use the Common Weakness Enumeration (CWE) as the label and perform fine-grained classification using categories at the third level of the CWE tree. We first formulate the task as a Hierarchical Multi-label Classification (HMC) problem, …
Niche: A Curated Dataset Of Engineered Machine Learning Projects In Python, Ratnadira Widyasari, Zhou Yang, Ferdian Thung, Sheng Qin Sim, Fiona Wee, Camellia Lok, Jack Phan, Haodi Qi, Constance Tan, David Lo, David Lo
Niche: A Curated Dataset Of Engineered Machine Learning Projects In Python, Ratnadira Widyasari, Zhou Yang, Ferdian Thung, Sheng Qin Sim, Fiona Wee, Camellia Lok, Jack Phan, Haodi Qi, Constance Tan, David Lo, David Lo
Research Collection School Of Computing and Information Systems
Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This …
Improved Marginal Likelihood Estimation Via Power Posteriors And Importance Sampling, Yong Li, Nianling Wang, Jun Yu
Improved Marginal Likelihood Estimation Via Power Posteriors And Importance Sampling, Yong Li, Nianling Wang, Jun Yu
Research Collection School Of Economics
Power posteriors have become popular in estimating the marginal likelihood of a Bayesian model. A power posterior is referred to as the posterior distribution that is proportional to the likelihood raised to a power b∈[0,1]. Important power-posterior-based algorithms include thermodynamic integration (TI) of Friel and Pettitt (2008) and steppingstone sampling (SS) of Xie et al. (2011). In this paper, it is shown that the Bernstein–von Mises (BvM) theorem holds for power posteriors under regularity conditions. Due to the BvM theorem, power posteriors, when adjusted by the square root of the auxiliary constant, have the same limit distribution as the original …
Uniform Nonparametric Inference For Time Series Using Stata, Jia Li, Zhipeng Liao, Mengsi Gao
Uniform Nonparametric Inference For Time Series Using Stata, Jia Li, Zhipeng Liao, Mengsi Gao
Research Collection School Of Economics
In this article, we introduce a command, tssreg, that conducts nonparametric series estimation and uniform inference for time-series data, including the case with independent data as a special case. This command can be used to nonparametrically estimate the conditional expectation function and the uniform confidence band at a user-specified confidence level, based on an econometric theory that accommodates general time-series dependence. The uniform inference tool can also be used to perform nonparametric specification tests for conditional moment restrictions commonly seen in dynamic equilibrium models.
Teaching In The Right Context: Textbook Supply Program, Language, And Vocabulary Ability In Vietnam, Tomoki Fujii, Maki Nakajima, Sijia Xu
Teaching In The Right Context: Textbook Supply Program, Language, And Vocabulary Ability In Vietnam, Tomoki Fujii, Maki Nakajima, Sijia Xu
Research Collection School Of Economics
An ethnic gap in education is prevalent around the world. This remains the case in Vietnam, a country that has achieved phenomenal economic growth and raised the educational attainment of the public. This paper examines the impact of language policy reorientation represented by the textbook supply program in Vietnam on the ethnic gap in children's learning measured by a vocabulary test. Applying difference-in-differences estimation to the Young Lives data between 2006 and 2015, we show that the program became more effective in narrowing the ethnic gap as the education policy became reoriented toward ethnic minority children. A causal mediation analysis …
Compositional Prompt Tuning With Motion Cues For Open-Vocabulary Video Relation Detection, Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun
Compositional Prompt Tuning With Motion Cues For Open-Vocabulary Video Relation Detection, Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun
Research Collection School Of Computing and Information Systems
Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary prediction trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. In particular, we present Relation Prompt (RePro) for Open-vocabulary Video Visual Relation Detection (Open-VidVRD), where conventional prompt tuning is easily biased to certain subject-object combinations and motion patterns. To this end, RePro addresses the two technical challenges of Open-VidVRD: 1) the prompt tokens should respect the two different semantic roles of subject and object, and 2) the tuning …
Reinforced Adaptation Network For Partial Domain Adaptation, Keyu Wu, Min Wu, Zhenghua Chen, Ruibing Jin, Wei Cui, Zhiguang Cao, Xiaoli Li
Reinforced Adaptation Network For Partial Domain Adaptation, Keyu Wu, Min Wu, Zhenghua Chen, Ruibing Jin, Wei Cui, Zhiguang Cao, Xiaoli Li
Research Collection School Of Computing and Information Systems
Domain adaptation enables generalized learning in new environments by transferring knowledge from label-rich source domains to label-scarce target domains. As a more realistic extension, partial domain adaptation (PDA) relaxes the assumption of fully shared label space, and instead deals with the scenario where the target label space is a subset of the source label space. In this paper, we propose a Reinforced Adaptation Network (RAN) to address the challenging PDA problem. Specifically, a deep reinforcement learning model is proposed to learn source data selection policies. Meanwhile, a domain adaptation model is presented to simultaneously determine rewards and learn domain-invariant feature …
Picaso: Enhancing Api Recommendations With Relevant Stack Overflow Posts, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo
Picaso: Enhancing Api Recommendations With Relevant Stack Overflow Posts, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo
Research Collection School Of Computing and Information Systems
While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowadays, API has become imperative in making software developers' life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of open-source libraries to choose from, choosing the right APIs is not a simple task. Previous studies on API recommendation leverage natural language (query) to identify which API would be suitable for the given task. However, these studies only consider one source of input, i.e., GitHub or …
On-Device Deep Multi-Task Inference Via Multi-Task Zipping, Xiaoxi He, Xu Wang, Zimu Zhou, Jiahang Wu, Zheng Yang, Lothar Thiele
On-Device Deep Multi-Task Inference Via Multi-Task Zipping, Xiaoxi He, Xu Wang, Zimu Zhou, Jiahang Wu, Zheng Yang, Lothar Thiele
Research Collection School Of Computing and Information Systems
Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks locally on-device. Yet the complexity of these deep models needs to be trimmed down both within-model and cross-model to fit in mobile storage and memory. Previous studies squeeze the redundancy within a single model. In this work, we aim to reduce the redundancy across multiple models. We propose Multi-Task Zipping (MTZ), a framework to automatically merge correlated, pre-trained deep neural networks for cross-model compression. Central in MTZ is a layer-wise neuron sharing and incoming weight updating scheme …
Trustworthy And Synergistic Artificial Intelligence For Software Engineering: Vision And Roadmaps, David Lo
Trustworthy And Synergistic Artificial Intelligence For Software Engineering: Vision And Roadmaps, David Lo
Research Collection School Of Computing and Information Systems
For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engiueering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to …
She Elicits Requirements And He Tests: Software Engineering Gender Bias In Large Language Models, Christoph Treude, Hideaki Hata
She Elicits Requirements And He Tests: Software Engineering Gender Bias In Large Language Models, Christoph Treude, Hideaki Hata
Research Collection School Of Computing and Information Systems
Implicit gender bias in software development is a well-documented issue, such as the association of technical roles with men. To address this bias, it is important to understand it in more detail. This study uses data mining techniques to investigate the extent to which 56 tasks related to software development, such as assigning GitHub issues and testing, are affected by implicit gender bias embedded in large language models. We systematically translated each task from English into a genderless language and back, and investigated the pronouns associated with each task. Based on translating each task 100 times in different permutations, we …
Applying Information Theory To Software Evolution, Adriano Torres, Sebastian Baltes, Christoph Treude, Markus Wagner
Applying Information Theory To Software Evolution, Adriano Torres, Sebastian Baltes, Christoph Treude, Markus Wagner
Research Collection School Of Computing and Information Systems
Although information theory has found success in disciplines, the literature on its applications to software evolution is limit. We are still missing artifacts that leverage the data and tooling available to measure how the information content of a project can be a proxy for its complexity. In this work, we explore two definitions of entropy, one structural and one textual, and apply it to the historical progression of the commit history of 25 open source projects. We produce evidence that they generally are highly correlated. We also observed that they display weak and unstable correlations with other complexity metrics. Our …
Stop Words For Processing Software Engineering Documents: Do They Matter, Yaohou Fan, Chetan Arora, Christoph Treude
Stop Words For Processing Software Engineering Documents: Do They Matter, Yaohou Fan, Chetan Arora, Christoph Treude
Research Collection School Of Computing and Information Systems
Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. However, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There is an ongoing debate among academics about the usefulness of stop word elimination, especially in domainspecific settings. In this work, we investigate the usefulness of stop word removal in a software engineering context. To do this, we replicate and experiment with three software engineering research tools from related work. Additionally, we construct a corpus of software engineering domain-related text from 10,000 Stack Overflow questions and …