Accelerating drug discovery requires more efficient strategies to optimize lead compounds. Scaffold decoration is a key method in lead optimization, helping to improve ligand binding affinity and synthetic feasibility while retaining the core structure. However, most of the existing scaffold decoration models are based on ligands and cannot adequately consider the target, as well as the affinity between the target and the ligands. In this work, we present ADOptDiff, a novel affinity-driven scaffold decoration framework grounded in diffusion probabilistic models. The model incorporates E(3)-equivariant graph neural networks, conditional generative diffusion processes, and an Affinity Driven Cross-Attention module (ADCA), enabling context-sensitive scaffold decoration within target pockets and facilitating affinity-driven optimization. We constructed a dataset of 75,348 entries from the BindingNet platform, each comprising a protein pocket, scaffold, R-chain, and corresponding ligand–target binding affinity. Compared with existing molecular generation models, benchmark results indicate that ADOptDiff outperforms across multiple key metrics. In particular, it achieves about 5% improvement in enhancing protein–ligand binding interactions, demonstrating strong potential for affinity-driven molecular design. Further case studies involving targets from the central nervous system, viral enzymes, and membrane-associated proteins, validated by molecular dynamics simulations, confirm the structural stability and binding efficacy of the generated molecules. We have made all the code and a subset of the data available at https://github.com/chenshengneng/ADOptDiff.git.

One main challenge in time series anomaly detection for industrial IoT lies in the complex spatio-temporal couplings within multivariate data. However, as traditional anomaly detection methods focus on modeling spatial or temporal dependencies independently, resulting in suboptimal representation learning and limited sensitivity to anomalous dispersion in high-dimensional spaces. In this work, we conduct an empirical analysis showing that both normal and anomalous samples tend to scatter in high-dimensional space, especially anomalous samples are markedly more dispersed. We formalize this dispersion phenomenon as scattering, quantified by the mean pairwise distance among sample representations, and leverage it as an inductive signal to enhance spatio-temporal anomaly detection. Technically, we propose ScatterAD to model representation scattering across temporal and topological dimensions. ScatterAD incorporates a topological encoder for capturing graph-structured scattering and a temporal encoder for constraining over-scattering through mean squared error minimization between neighboring time steps. We introduce a contrastive fusion mechanism to ensure the complementarity of the learned temporal and topological representations. Additionally, we theoretically show that maximizing the conditional mutual information between temporal and topological views improves cross-view consistency and enhances more discriminative representations. Extensive experiments on multiple public benchmarks show that ScatterAD achieves state-of-the-art performance on multivariate time series anomaly detection.

Exploring drug-target binding affinity (DTA) is essential for drug discovery. Numerous works rely on the one-dimensional SMILES representation of drugs for predicting drug-target affinity, but ignore the crucial structural information of drug molecules. Considering structural information is an important factor in determining the affinity properties of drugs, we propose using the multiscale motif-aware relation graph (MMRG) rather than the SMILES representation to build drug descriptors. MMRG explicitly provides crucial motif-level structural and topological information of drugs, thereby ameliorating the predictive power of models. With this idea, we propose a novel MMRG construction approach for drugs, including a multiscale motif-aware learning for extracting motif-level structural information from motifs with different sizes and a relation graph construction approach for extracting topological information from chemical bonds. We implement a graph convolutional network to learn from MMRGs, and the learned latent features are used to predict drug-target affinity. The experiment results on the Davis and KIBA dataset report that our model can significantly outperform existing methods in drug-target affinity prediction, with an average improvement of 15.61% and 8.50% respectively. We further explain the principle of MMRG to improve the drug-target affinity prediction accuracy by comparing its generative function with the state-of-the-art method.

Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise. In this paper, we present the Probabilistic Deep Supervision Network (PDS-Net), a robust framework designed to effectively identify and mitigate feature noise, thereby improving QoS prediction accuracy. PDS-Net operates with adual-branch architecture: the main branch utilizes a decoder network to learn a Gaussian-based prior distribution from known features, while the second branch derives a posterior distribution based on true labels. A key innovation of PDS-Net is its condition-based noise recognition loss function, which enables precise identification of noisy features in objects (users or services). Once noisy features are identified, PDS-Net refines the feature's prior distribution, aligning it with the posterior distribution, and propagates this adjusted distribution to intermediate layers, effectively reducing noise interference. Extensive experiments conducted on two real-world QoS datasets demonstrate that PDS-Net consistently outperforms existing models, achieving an average improvement of 8.91% in MAE on Dataset D1 and 8.32% on Dataset D2 compared to the state-of-the-art. These results highlight PDS-Net's ability to accurately capture complex user-service relationships and handle feature noise, underscoring its robustness and versatility across diverse QoS prediction environments.

Accurate prediction of Quality of Service (QoS) metrics is fundamental for selecting and managing cloud-based services. Traditional QoS models rely on manual feature engineering and yield only point estimates, offering no insight into the confidence of their predictions. In this paper, we propose QoSBERT, the framework that reformulates QoS prediction as a semantic regression task based on pre-trained language models. Unlike previous approaches relying on sparse numerical features, QoSBERT automatically encodes user-service metadata into natural language descriptions, enabling deep semantic understanding. Furthermore, we integrate a Monte Carlo Dropout–based uncertainty estimation module, allowing for trustworthy and risk-aware service quality prediction, which is crucial yet underexplored in existing QoS models. QoSBERT encodes user-service metadata as natural language and leverages a pre-trained model to capture contextual semantics. It applies attentive pooling over the encoded embeddings and employs a lightweight regressor optimized to minimize prediction error. To quantify predictive confidence, Monte Carlo Dropout is applied at inference time. The resulting uncertainty estimates further support high-confidence sample selection, enhancing robustness in low-resource scenarios. On standard QoS benchmark datasets, QoSBERT achieves an average reduction of 11.7% in MAE and 6.7% in RMSE for response time prediction, and 6.9% in MAE for throughput prediction compared to the strongest baselines, while providing well-calibrated confidence intervals for robust and trustworthy service quality estimation. Our approach not only advances the accuracy of service quality prediction but also delivers reliable uncertainty quantification, paving the way for more trustworthy, datadriven service selection and optimization.

Large Language Models (LLMs) have been widely adopted by developers in software development. However, the massive pretraining code data is not rigorously filtered, allowing LLMs to learn unsafe coding patterns. Several prior studies have demonstrated that code LLMs tend to generate code with potential vulnerabilities. The widespread adoption of intelligent programming assistants poses a significant threat to the software development process. Existing approaches to mitigating this risk primarily involve constructing secure data that are free of vulnerabilities and then retraining or fine-tuning the models. However, such an effort is resource intensive and requires significant manual supervision. When the model parameters are too large (e.g., more than 1 billion) or multiple models with the same parameter scale have the same optimization needs (e.g., to avoid outputting vulnerable code), the above work will become unaffordable. To address this challenge, in previous work, we proposed CoSec, an approach to improve the security of code LLMs with different parameters by utilizing an independent and very small parametric security model as a decoding navigator.Despite CoSec’s excellent performance, we found that there is still room for improving: 1) its ability to maintain the functional correctness of hardened targets, and 2) the security of the generated code. To address the above issues, we propose CoSec+, a hardening framework consisting of three phases: 1) Functional Correctness Alignment, which improves the functional correctness of the security base with knowledge disstillation; 2) Security Training, which yields an independent, but much smaller security model; and 3) Co-decoding, where the security model iteratively reasons about the next token along with the target model. Due to the higher confidence that a well-trained security model places in secure and correct tokens, it guides the target base model to generate more secure code, even as it improves the functional correctness of the target base model. We have conducted extensive experiments in several code LLMs (i.e., CodeGen, StarCoderBase, DeepSeekCoder and Qwen2.5-Coder), and the results show that our approach is effective in improving the functional correctness and security of the models. The evaluation results show that CoSec+ can deliver a 0.8% to 37.7% improvement in security across models of various parameter sizes and families; moreover, it preserves the functional correctness of the target base models—achieving functional-correctness gains of 0.7% to 51.1% for most of those models.

Unit Testing is crucial in software development and maintenance, aiming to verify that the implemented functionality is consistent with the expected functionality. A unit test is composed of two parts: a test prefix, which drives the unit under test to a particular state, and a test assertion, which determines what the expected behavior is under that state. To reduce the effort of conducting unit tests manually, Yu et al. proposed an integrated approach (integrationfor short), combining information retrieval with a deep learning-based approach to generate assertions for test prefixes, and obtained promising results. In our previous work, we found that the overall performance ofintegrationis mainly due to its success in retrieving assertions. Moreover,integrationis limited to specific types of edit operations and struggles to understand the semantic differences between the retrieved focal-test (focal-testincludes a test prefix and a unit under test) and the input focal-test. Based on these insights, we then proposed a retrieve-and-edit approach namedEditAS to learn the assertion edit patterns to improve the effectiveness of assertion generation in our prior study. Despite being promising, we find that the effectiveness ofEditAS can be further improved. Our analysis shows that: ① The editing ability ofEditAS still has ample room for improvement. Its performance degrades as the edit distance between the retrieval assertion and ground truth increases. Specifically, the average accuracy ofEditAS is 12.38% when the edit distance is greater than 5. ②EditAS lacks a fine-grained semantic understanding of both the retrieved focal-test and the input focal-test themselves, which leads to many inaccurate token modifications. In particular, an average of 25.57% of the incorrectly generated assertions that need to be modified are not modified, and an average of 6.45% of the assertions that match the ground truth are still modified. Thanks to pre-trained models employing pre-training paradigms on large-scale data, they tend to have good semantic comprehension and code generation abilities. In light of this, we proposeEditAS2, which improves retrieval-and-edit based assertion generation through retrieval-augmented fine-tuning. Specifically,EditAS2first retrieves a similar focal-test from a predefined corpus and treats its assertion as a prototype. Then,EditAS2uses a pre-trained model, CodeT5, to learn the semantics of the input and similar focal-tests as well as assertion editing patterns to automatically edit the prototype. We first evaluate theEditAS2for its inference performance on two large-scale datasets, and the experimental results show thatEditAS2outperforms state-of-the-art assertion generation methods and pre-trained models, with average performance improvements of 15.93%-129.19% and 11.01%-68.88% in accuracy and CodeBLEU, respectively. We also evaluate the performance ofEditAS2in detecting real-world bugs from Defects4J. The experimental results indicate thatEditAS2achieves the best bug detection performance among all the methods.

Biochemical methods for measuring drug-target binding are costly and slow, while deep learning offers a crucial solution. Deep learning methods for predicting drug-target binding affinity have achieved remarkable success, but existing approaches face challenges: adjacent elements in a three-dimensional structure may be far apart in a one-dimensional representation, and long sequences of drug and target data often contain lots of noise. In this paper, we introduce MambaDTA, a novel architecture for drug-target affinity prediction based on the State Space Model (SSM). Mamba-DTA utilizes SSM to model the drug molecules and target molecules and extract more discriminative spatial structural features efficiently and stably. Additionally, we design Interaction-based Selective Filtering (ISF) module to model drug-target interactions and filter out redundant information. The experimental results on two publicly available datasets, namely Davis and KIBA, demonstrate the effectiveness and superiority of our Mamba-DTA. Specifically, Mamba-DTA achieves a relative gain of 13.3% in terms of MAE on the Davis dataset. Source codes are available at https://github.com/202324131016T/Mamba-DTA.

To improve software development productivity, developers frequently search for projects on open-source communities such as GitHub. However, it is challenging for users to quickly find suitable projects from numerous results due to the overload of project information. Although many tools have been proposed to rank the relevancy of searched results, manually inspecting them one by one is irreplaceable and time-consuming. To fill this gap, we propose a visual retrieval tool named VisRepo for open-source software projects. Firstly, it mines software project data from four perspectives including topic, technology, usability, and comprehensibility, and connects projects based on the same owners/contributors and similar topics. Then, visualization technique is employed to present complex software data intuitively. VisRepo provides users an interactive retrieval paradigm of Search-Explore-Check-Recommend with in-depth insights and better exploration experience. We evaluate VisRepo on 7w+ open-source JavaScript projects. Experimental results showed that VisRepo outperforms GitHub search engine in terms of time consumption and accuracy, meanwhile enabling a more interactive and useful user experience.

Large Language Models (LLMs) specialized in code have shown exceptional proficiency across various programming-related tasks, particularly code generation. Nonetheless, due to its nature of pretraining on massive uncritically filtered data, prior studies have shown that code LLMs are prone to generate code with potential vulnerabilities. Existing approaches to mitigate this risk involve crafting data without vulnerability and subsequently retraining or fine-tuning the model. As the number of parameters exceeds a billion, the computation and data demands of the above approaches will be enormous. Moreover, an increasing number of code LLMs tend to be distributed as services, where the internal representation is not accessible, and the API is the only way to reach the LLM, making the prior mitigation strategies non-applicable. To cope with this, we propose CoSec, an on-the-fly Security hardening method of code LLMs based on security model-guided Co-decoding, to reduce the likelihood of code LLMs to generate code containing vulnerabilities. Our key idea is to train a separate but much smaller security model to co-decode with a target code LLM. Since the trained secure model has higher confidence for secure tokens, it guides the generation of the target base model towards more secure code generation. By adjusting the probability distributions of tokens during each step of the decoding process, our approach effectively influences the tendencies of generation without accessing the internal parameters of the target code LLM. We have conducted extensive experiments across various parameters in multiple code LLMs (i.e., CodeGen, StarCoder, and DeepSeek-Coder), and the results show that our approach is effective in security hardening. Specifically, our approach improves the average security ratio of six base models by 5.02%-37.14%, while maintaining the functional correctness of the target model.

Excessive non-actionable warnings generated by static program analysis tools can hinder developers from utilizing these tools effectively. Leveraging learning-based approaches for actionable warning identification has demonstrated promise in boosting developer productivity, minimizing the risk of bugs, and reducing code smells. However, the small sizes of existing datasets have limited the model choices for machine learning researchers, and the lack of aligned fix commits limits the scope of the dataset for research. In this paper, we present AW4C, an actionable warning C dataset that contains 38,134 actionable warnings mined from more than 500 repositories on GitHub. These warnings are generated via Cppcheck, and most importantly, each warning is precisely mapped to the commit where the corrective action occurred. To the best of our knowledge, this is the largest publicly available actionable warning dataset for C programming language to date. The dataset is suited for use in machine/deep learning models and can support a wide range of tasks, such as actionable warning identification and vulnerability detection. Furthermore, we have released our dataset1 and a general framework for collecting actionable warnings on GitHub2 to facilitate other researchers to replicate our work and validate their innovative ideas.

Automated code generation is a powerful technique for software development, which can significantly reduce developers' effort and time for writing code. Recently, OpenAI's large language model ChatGPT has emerged as a powerful tool for generating human-like responses to a wide range of textual inputs (i.e., prompts), including those related to code generation. However, the effectiveness of ChatGPT in code generation is still not well understood. The code generation performance could also be heavily influenced by the choice of prompts, which should be further explored. In this paper, we report an empirical study on ChatGPT's capabilities for two types of code generation tasks, namely text-to-code and code-to-code generation. We investigate different types of prompts by leveraging the chain-of-thought strategy with multi-step optimizations. Our empirical results show that by carefully designing prompts to guide ChatGPT, the code generation performance can be improved substantially. We also analyze the factors that influence the prompt design and provide insights that could guide future research.

Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to leverage the valuable information inherent in the consensus and disagreements among the multiple annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation. To this end, we introduce the Multi-Confidence Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence (HC) Mask. The LC mask indicates regions with low segmentation confidence, where radiologists may have different segmentation choices. Following UAAM, we further design an Uncertainty-Guide Multi-Confidence Segmentation Network (UGMCS-Net), which contains three modules: a Feature Extracting Module that captures a general feature of a lung nodule, an Uncertainty-Aware Module that produces three features for the annotations' union, intersection, and annotation set, and an Intersection-Union Constraining Module that uses distances between the three features to balance the predictions of final segmentation and MCM. To comprehensively demonstrate the performance of our method, we propose a Complex-Nodule Validation on LIDC-IDRI, which tests UGMCS-Net's segmentation performance on lung nodules that are difficult to segment using common methods. Experimental results demonstrate that our method can significantly improve the segmentation performance on nodules that are difficult to segment using conventional methods.

Cloud service providers often provision excessive resources to meet the desired Service Level Objectives (SLOs), by setting lower CPU utilization targets. This can result in a waste of resources and a noticeable increase in power consumption in large-scale cloud deployments. To address this issue, this paper presents DeepScaling, an innovative solution for minimizing resource cost while ensuring SLO requirements are met in a dynamic, large-scale production microservice-based system. We propose DeepScaling, which introduces three innovative components to adaptively refine the target CPU utilization of servers in the data center, and we maintain it at a stable value to meet SLO constraints while using minimum amount of system resources. First, DeepScaling forecasts workloads for each service using a Spatio-temporal Graph Neural Network. Secondly, it estimates CPU utilization with a Deep Neural Network, considering factors such as periodic tasks and traffic. Finally, it uses a modified Deep Q-Network (DQN) to generate an autoscaling policy that controls service resources to maximize service stability while meeting SLOs. Evaluation of DeepScaling in Ant Group’s large-scale cloud environment shows that it outperforms state-of-the-art autoscaling approaches in terms of maintaining stable performance and resource savings. The deployment of DeepScaling in the real-world environment of 1900+ microservices saves the provisioning of over 100,000 CPU cores per day, on average.

With the increasing number of services and their homogenization, the use of Quality of Service (QoS) for recommendations has become necessary. However, existing QoS prediction solutions have limitations in solving the noise and label imbalance problems of dataset, which greatly limit the improvement of QoS prediction accuracy. In this paper, we propose FSNet that contains a feature distribution smoothing module and an improved W-Huber loss function. The feature distribution smoothing module mitigates the effect of noise problem by fitting potential Gaussian distribution of known features with a supervised feedforward neural network. W-Huber loss function mitigates the impact of label imbalance problem on QoS prediction by reweighting the two components of Huber loss function. We conduct extensive experiments on real large-scale QoS dataset, and the results demonstrate that the proposed FSNet method outperforms existing QoS prediction methods.

Abstract Deep‐Learning‐based Fault Localisation (DLFL) leverages deep neural networks to learn the relationship between statement behaviour and program failures, showing promising results. However, since DLFL uses program failures as labels to conduct supervised learning, a labelled dataset is a requisite of applying DLFL. A failure is detected by comparing program output with a test oracle which is the standard answer for the given input. The problem is, test oracles are often difficult, or even impossible to acquire in real life, and that has severely restricted the application of DLFL since we have only unlabelled datasets in most cases. Thus, MetaFL: Metamorphic Fault Localisation Using Weakly Supervised Deep Learning is proposed, to provide a weakly supervised learning solution for DLFL. Instead of using test oracles, MetaFL uses metamorphic relations to prescribe expected behaviour of a program, and defines labels of metamorphic testing groups by verifying integrity in each group of test cases. Hence, a coarse‐grained labelled dataset can be built from the originally unlabelled one, with which DLFL can work now, utilising a weakly supervised learning paradigm. The experiments show that MetaFL yields a performance comparable to plain DLFL under ideal condition (i.e. the labels of datasets are available). MetaFL successfully extends the methodology of DLFL from supervised learning to weakly supervised learning, and a fully labelled dataset is no longer mandatory for applying DLFL.

Automatic medical image segmentation plays a crucial role in clinical diagnosis and treatment. However, it is still a challenging task due to the complex interior characteristics ( e.g. , inconsistent intensity, low contrast, texture heterogeneity) and ambiguous external boundary structures. In this paper, we introduce a novel geometric structure learning mechanism (GSLM) to overcome the limitations of existing segmentation models that lack learning ”focus, path, and difficulty.” The geometric structure in this mechanism is jointly characterized by the skeleton-like structure extracted by the mask distance transform (MDT) and the boundary structure extracted by the mask distance inverse transform (MDIT). Among them, the skeleton-like and boundary pay attention to the trend of interior characteristics consistency and external structure continuity, respectively. With this idea, we design GSAL, a novel end-to-end geometric structure adversarial learning for robust medical image segmentation. GSAL has four components: a geometric structure generator, which yields the geometric structure to learn the most discriminative features that preserve interior characteristics consistency and external boundary structure continuity, skeleton-like and boundary structure discriminators , which enhance and correct the characterization of internal and external geometry to mutually promote the capture of global contextual dependencies, and a geometric structure fusion sub-network, which fuses the two complementary and refined skeleton-like and boundary structures to generate the high-quality segmentation results. The proposed approach has been successfully applied to three different challenging medical image segmentation tasks , including polyp segmentation , COVID-19 lung infection segmentation, and lung nodule segmentation. Extensive experimental results demonstrate that the proposed GSAL achieves favorably against most state-of-the-art methods under different evaluation metrics . The code is available at: https://github.com/DLWK/GSAL .

Cloud service providers conservatively provision excessive resources to ensure service level objectives (SLOs) are met. They often set lower CPU utilization targets to ensure service quality is not degraded, even when the workload varies significantly. Not only does this potentially waste resources, but it can also consume excessive power in large-scale cloud deployments. This paper aims to minimize resource costs while ensuring SLO requirements are met in a dynamically varying, large-scale production microservice environment. We propose DeepScaling, which introduces three innovative components to adaptively refine the target CPU utilization to a level that is maintained at a stable value to meet SLO constraints while using minimum resources. First, DeepScaling forecasts the workload for each service using a Spatio-temporal Graph Neural Network. Second, DeepScaling estimates the CPU utilization by mapping the workload intensity to an estimated CPU utilization with a Deep Neural Network, while taking into account multiple factors in the cloud environment (e.g., periodic tasks and traffic). Third, DeepScaling generates an autoscaling policy for each service based on an improved Deep Q Network (DQN). The adaptive autoscaling policy updates the target CPU utilization to be a maximum, stable value, while ensuring SLOs is not violated. We compare DeepScaling with state-of-the-art autoscaling approaches in the large-scale production cloud environment of the Ant Group. It shows that DeepScaling outperforms other approaches both in terms of maintaining stable service performance, and saving resources, by a significant margin. The deployment of DeepScaling in Ant Group's real production environment with 135 microservices saves the provisioning of over 30,000 CPU cores per day, on average.

Software architecture is undergoing a transition from monolithic architectures to microservices to achieve resilience, agility, and scalability in the software life circle. However, microservice architecture is not perfect and suffers from intermittent faults, leading to economic and user losses. Therefore, it is essential to detect anomalies in microservice systems accurately. The key limitation of current approaches lies in a lack of ability to detect multitype anomalies, excessive resource overhead, and requirements of expert knowledge. In this paper, we present a Deep Attentive anomaly detection approach with Multimodal data named DAM. With multimodal fusion, attentive LSTM, and a dynamic threshold selecting algorithm, DAM could detect anomalies accurately and efficiently in an unsupervised manner. We evaluate our approach by injecting six types of anomalies on a widely used microservice system, Train-Ticket. The result shows that DAM could detect multitype anomalies well, with 80.46% F-measure, achieving 16.76% and 29.52% improvement over two state-of-the-art baselines (Donut and DAGMM), respectively.

Semantic segmentation tasks often have multiple output hypotheses for a single input image. Particularly in medical images, these ambiguities arise from unclear object boundaries or differences in physicians' annotation. Learning the distribution of annotations and automatically giving multiple plausible predictions is useful to assist physicians in their decision-making. In this paper, we propose a semantic segmentation framework, PixelSeg, for modelling aleatoric uncertainty in segmentation maps and generating multiple plausible hypotheses. Unlike existing works, PixelSeg accomplishes the semantic segmentation task by sampling the segmentation maps pixel by pixel, which is achieved by the PixelCNN layers used to capture the conditional distribution between pixels. We propose (1) a hierarchical architecture to model high-resolution segmentation maps more flexibly, (2) a fast autoregressive sampling algorithm to improve sampling efficiency by 96.2, and (3) a resampling module to further improve predictions' quality and diversity. In addition, we demonstrate the great advantages of PixelSeg in the novel area of interactive uncertainty segmentation, which is beyond the capabilities of existing models. Extensive experiments and state-of-the-art results on the LIDC-IDRI and BraTS 2017 datasets demonstrate the effectiveness of our proposed model.

Medical image segmentation tasks often have more than one plausible annotation for a given input image due to its inherent ambiguity. Generating multiple plausible predictions for a single image is of interest for medical critical applications. Many methods estimate the distribution of the annotation space by developing probabilistic models to generate multiple hypotheses. However, these methods aim to improve the diversity of predictions at the expense of the more important accuracy. In this paper, we propose a novel probabilistic segmentation model, called Joint Probabilistic U-net, which successfully achieves flexible control over the two abstract conceptions of diversity and accuracy. Specifically, we (i) model the joint distribution of images and annotations to learn a latent space, which is used to decouple diversity and accuracy, and (ii) transform the Gaussian distribution in the latent space to a complex distribution to improve model's expressiveness. In addition, we explore two strategies for preventing the latent space collapse, which are effective in improving the model's performance on datasets with limited annotation. We demonstrate the effectiveness of the proposed model on two medical image datasets, i.e. LIDC-IDRI and ISBI 2016, and achieved state-of-the-art results on several metrics.

Logs are widely used for system behavior diagnosis by automatic log mining. Log parsing is an important data preprocessing step that converts semi-structured log messages into structured data as the feature input for log mining. Currently, many studies are devoted to proposing new log parsers. However, to the best of our knowledge, no previous study comprehensively investigates the effectiveness of log parsers in industrial practice. To investigate the effectiveness of the log parsers in industrial practice, in this paper, we conduct an empirical study on the effectiveness of six state-of-the-art log parsers on 10 microservice applications of Ant Group. Our empirical results highlight two challenges for log parsing in practice: 1) various separators. There are various separators in a log message, and the separators in different event templates or different applications are also various. Current log parsers cannot perform well because they do not consider various separators. 2) Various lengths due to nested objects. The log messages belonging to the same event template may also have various lengths due to nested objects. The log messages of 6 out of 10 microservice applications at Ant Group with various lengths due to nested objects. 4 out of 6 state-of-the-art log parsers cannot deal with various lengths due to nested objects. In this paper, we propose an improved log parser named Drain+ based on a state-of-the-art log parser Drain. Drain+ includes two innovative components to address the above two challenges: a statistical-based separators generation component, which generates separators automatically for log message splitting, and a candidate event template merging component, which merges the candidate event templates by a template similarity method. We evaluate the effectiveness of Drain+ on 10 microservice applications of Ant Group and 16 public datasets. The results show that Drain+ outperforms the six state-of-the-art log parsers on industrial applications and public datasets. Finally, we conclude the observations in the road ahead for log parsing to inspire other researchers and practitioners.

The high-precision QoS (quality of service) prediction is based on the comprehensive perception of state information of users and services. However, the current QoS prediction approaches have limited accuracy, for most state information of users and services (i.e., network speed, latency, network type, and more) are hidden due to privacy protection. Therefore, this article proposes a hidden-state-aware network (HSA-Net) that includes three steps called hidden state initialization, hidden state perception, and QoS prediction. A hidden state initialization approach is developed first based on the latent dirichlet allocation (LDA). After that, a hidden-state perception approach is proposed to abstract the initialized hidden state by fusing the known information (e.g., service ID and user location). The perception approach consists of four hidden-state perception (HSP) modes (i.e., known mode, object mode, hybrid mode and overall mode) implemented to generate explainable and fused features through four adaptive convolutional kernels. Finally, the relationship between the fused features and the QoS is discovered through a fully connected network to complete the high-precision QoS prediction process. The proposed HSA-Net is evaluated on two real-world datasets. According to the results, the HSA-Net's mean absolute error (MAE) index reduced by 3.67% and 28.84%, whereas the root mean squared error (RMSE) index decreased by 3.07% and 7.14% compared with ten baselines on average in the two datasets.

Just-in-time (JIT) bug prediction is an effective quality assurance activity that identifies whether a code commit will introduce bugs into the mobile app, aiming to provide prompt feedback to practitioners for priority review. Since collecting sufficient labeled bug data is not always feasible for some mobile apps, one possible approach is to leverage cross-app models. In this work, we propose a new cross-triplet deep feature embedding method, called CDFE, for cross-app JIT bug prediction task. The CDFE method incorporates a state-of-the-art cross-triplet loss function into a deep neural network to learn high-level feature representation for the cross-app data. This loss function adapts to the cross-app feature learning task and aims to learn a new feature space to shorten the distance of commit instances with the same label and enlarge the distance of commit instances with different labels. In addition, this loss function assigns higher weights to losses caused by cross-app instance pairs than that by intra-app instance pairs, aiming to narrow the discrepancy of cross-app bug data. We evaluate our CDFE method on a benchmark bug dataset from 19 mobile apps with two effort-aware indicators. The experimental results on 342 cross-app pairs show that our proposed CDFE method performs better than 14 baseline methods.

In this paper, we propose a novel feature decoupling method to tackle two critical problems in the lung nodule segmentation task: (i) ambiguity of nodule boundary leads to the imprecise segmentation boundary and (ii) the high false positive rate of segmentation result. Our motivation is that an accurate segmentation network needs explicitly modeling the nodule boundary and texture information, and suppressing the noise information. To do so, a novel Deep Feature Decoupling Module (DFDM) is proposed to decouple the nodule boundary, noise, and texture information from the original feature maps. The decoupled boundary and texture information is used to benefit the segmentation, and the noise information is removed from the input features to reduce the false positive rate. The proposed DFDM consists of three parallel branches, including Boundary Sensitive Branch (BSB), Noise Removal Branch (NRB), and Texture Preserving Branch (TPB) to decouple the mentioned three information, respectively. In particular, we design our BSB with a novel architecture to effectively capture the boundary information of lung nodules. We apply the proposed DFDM to the U-Net architecture and achieve convincing segmentation results on the LIDC–IDRI dataset. Code and models are available at https://github.com/chinichenw/DFDM.

Pulmonary nodule detection and segmentation are the necessary successively steps in lung cancer screening with low-dose computed tomography (CT) scans. However, the state-of-the-art models focus on solving tasks separately, thereby ignore the correlation between each task. Besides, most nodule detectors adopt anchor-based method falling to achieve good performance in low FPs per scan. To overcome those barriers, we present a novel multi-task 3D convolutional network (DeepNodule) for simultaneous nodule detection and segmentation in a shared-and-fined manner. Meanwhile, we utilize the center-point of the predicted segmentation masks to refine the bounding box coordinate and get a more precise nodule location. Furthermore, we design a 3D Gated Channel Transformation convolutional attention block for learning nodule features better. Experiments conducted on LUNA16 dataset demonstrates that DeepNodule obtains competitive performance, with the sensitivity of nodule candidate detection achieving 92.0%, and the accuracy of nodule segmentation reaching 80.04%.

Many medical images domains suffer from inherent ambiguities. A feasible approach to resolve the ambiguity of lung nodule in the segmentation task is to learn a distribution over segmentations based on a given 2D lung nodule image. Whereas lung nodule with 3D structure contains dense 3D spatial information, which is obviously helpful for resolving the ambiguity of lung nodule, but so far no one has studied it. To this end we propose a probabilistic generative segmentation model consisting of a V-Net and a conditional variational autoencoder. The proposed model obtains the 3D spatial information of lung nodule with V-Net to learn a density model over segmentations. It is capable of efficiently producing multiple plausible semantic lung nodule segmentation hypotheses to assist radiologists in making further diagnosis to resolve the present ambiguity. We evaluate our method on publicly available LIDC-IDRI dataset and achieves a new state-of-the-art result with 0.231±0.005 in $D_{GED}^2$. This result demonstrates the effectiveness and importance of leveraging the 3D spatial information of lung nodule for such problems. Code is available at: https://github.com/jiangjiangxiaolong/PV-Net.

Many automated commit message generation (CMG) approaches have been proposed for facilitating the understanding of software changes. They are shown to be promising and can generate commit messages that are semantically relevant to the reference messages for a number of commits. However, a large proportion (over 50%) of semantically irrelevant commit messages are also generated simultaneously. Such messages may mislead developers, require additional efforts of developers to confirm and filter out, and hinder the application of existing CMG approaches in practice. For tackling this problem, prior work mainly focuses on proposing new methods to improve the generation accuracy. However, another promising way for bridging the gap between CMG approaches and the practice has not been well investigated, which is: can we automatically assure the semantic relevance of the generated messages?To that end, in this work, we propose an automated Quality A ssurance framework for commit message generation (QAcom). QAcom can assure the quality of generated commit messages by automatically filtering out the semantically-irrelevant generated messages and preserving the semantically-relevant ones as many as possible. In particular, QAcom consists of a Collaborative-Filtering-based (CF) component and a Retrieval-based (RE) component. Given a commit message generated by a CMG approach, QAcom estimates whether this generated message is semantically relevant to its ground truth, which is unknown when estimating, based on both the collaborative filtering algorithm and the similarity between this commit and historical commits. We evaluate the effectiveness of QAcom by "plugging" it in three state-of-the-art CMG approaches. Experimental results on three public datasets show that QAcom can effectively filter out semantically-irrelevant generated messages and preserve semantically-relevant ones.

The important cues for a realistic lung nodule synthesis include the diversity in shape and background, controllability of semantic feature levels, and overall CT image quality. To incorporate these cues as the multiple learning targets, we introduce the Multi-Target Co-Guided Adversarial Mechanism, which utilizes the foreground and background mask to guide nodule shape and lung tissues, takes advantage of the CT lung and mediastinal window as the guidance of spiculation and texture control, respectively. Further, we propose a Multi-Target Co-Guided Synthesizing Network with a joint loss function to realize the co-guidance of image generation and semantic feature learning. The proposed network contains a Mask-Guided Generative Adversarial Sub-Network (MGGAN) and a Window-Guided Semantic Learning Sub-Network (WGSLN). The MGGAN generates the initial synthesis using the mask combined with the foreground and background masks, guiding the generation of nodule shape and background tissues. Meanwhile, the WGSLN controls the semantic features and refines the synthesis quality by transforming the initial synthesis into the CT lung and mediastinal window, and performing the spiculation and texture learning simultaneously. We validated our method using the quantitative analysis of authenticity under the Fréchet Inception Score, and the results show its state-of-the-art performance. We also evaluated our method as a data augmentation method to predict malignancy level on the LIDC-IDRI database, and the results show that the accuracy of VGG-16 is improved by 5.6%. The experimental results confirm the effectiveness of the proposed method.

Accurate identification and early diagnosis of malignant lung nodules are crucial for improving the survival rate of patients with lung cancer. Deep learning methods have recently been proven success in computer-aided diagnostic tasks. However, to the best of our knowledge, the features of tissues and vessels will disturb the model resulting in inaccurate classification of the nodules. To reduce the interference and capture crucial contextual information from different channels in a more efficient way, we introduce a Hyper-Attention Mechanism(HAM) that can be easily integrated into convolutional neural networks(CNNs). Moreover, without incorporating prior-domain knowledge, traditional methods lack interpretability, which is difficult to understand and utilize them in the clinic by radiologists. Based on this, we propose a novel Knowledge-Guided model to predict malignant pulmonary nodules from chest CT data, which inject external medical knowledge into CNNs to guide the training process. We evaluate the proposed model on the LIDC-IDRI dataset and demonstrate its effectiveness by achieving comparable state-of-the-art performance.

Accurate segmentation for lung nodules in lung computed tomography (CT) scans plays a key role in the early diagnosis of lung cancer. Many existing methods, especially U-Net, have made significant progress in lung nodule segmentation. However, due to the complex shapes of lung nodules and the similarity of visual characteristics between nodules and lung tissues, an accurate segmentation with low false positives of lung nodules is still a challenging problem. Considering the fact that both boundary and texture information of lung nodules are important for obtaining an accurate segmentation result, we propose a novel Mask and Texture-driven Generative Adversarial Network (MTGAN) with a joint multi-scale L1 loss for lung nodule segmentation, which takes full advantages of U-Net and adversarial training. The proposed MTGAN leverages adversarial learning strategy guided by the boundary and texture information of lung nodules to generate more accurate segmentation results with lesser false positives. We validate our model with the LIDC-IDRI dataset, and experimental results show that our method achieves excellent segmentation results for a variety of lung nodules, especially for juxtapleural nodules and low-dense nodules. Without any bells and whistles, the proposed MTGAN achieves significant segmentation performance with the Dice similarity coefficient (DSC) of 85.24% on the LIDC-IDRI dataset.

Computer-Aided Diagnosis (CAD) systems for lung nodule diagnosis based on deep learning have attracted much attention in recent years. However, most existing methods ignore the relationships between the segmentation and classification tasks, which leads to unstable performances. To address this problem, we propose a novel multi-task framework, which can provide lung nodule segmentation mask, malignancy prediction, and medical features for interpretable diagnosis at the same time. Our framework mainly contains two sub-network: (1) Multi-Channel Segmentation Sub-network (MSN) for lung nodule segmentation, and (2) Joint Classification Sub-network (JCN) for interpretable lung nodule diagnosis. In the proposed framework, we use U-Net down-sampling processes for extracting low-level deep learning features, which are shared by two sub-networks. The JCN forces the down-sampling processes to learn better low-level deep features, which lead to a better construct of segmentation masks. Meanwhile, two additional channels constructed by OTSU and super-pixel (SLIC) methods, are utilized as the guideline of the feature extraction. The proposed framework takes advantages of deep learning methods and classical methods, which can significantly improve the performances of all tasks. We evaluate the proposed framework on public dataset LIDC-IDRI. Our framework achieves a promising Dice score of 86.43% in segmentation, 87.07% in malignancy level prediction, and convincing results in interpretable medical feature predictions.

Logs are universally available in software systems for troubleshooting. They record system run-time states and messages of system activities. Log analysis is an effective way to diagnosis system exceptions, but it will take a long time for engineers to locate anomalies accurately through logs. Many automatic approaches have been proposed for log-based anomaly detection. However, most of the prior approaches did not consider the corresponding system component of a log message. Such component records the log location, which can help detect the location-sequence-related anomalies. In this paper, we propose LogC, a new Log -based anomaly detection approach with Component-aware analysis. LogC contains two phases: (i) turning log messages into log template sequences and component sequences, (ii) feeding such two sequences to train a combined LSTM model for detecting anomalous logs. LogC only needs normal log sequences to train the combined model. We evaluate LogC on two open-source log datasets: HDFS and ThunderBird. Experimental results show that LogC overall outperforms three baselines (i.e., PCA, IM, and DeepLog) in terms of three metrics (precision, recall, and F-measure).

This paper aims to automatically diagnose thoracic diseases in Chest X-ray(CXR) images using deep neural net-works(DNN). However, the existing approaches generally use the global CXR images as input for training purposes. This strategy is low-efficiency, coarse, and might introduce many unnecessary noises. We believe that the deep learning, which is inherently an algebraic computation system, is not the most efficient way to acquire highly sophisticated human knowledge, for example those thoracic diseases are typically limited within the lung regions and interdependence between lesion location. In this paper, we address the above problem by proposing to explore how external medical knowledge can be injected into DNN to guide its training process. We design four feature extraction modules to construct a knowledge-guided deep zoom neural network(KGZNet), which can gradually make full use of the most medical discriminative feature information(from coarse to fine) of global, lung regions, and lesion regions. Specifically, we first learn global branch using global images. Second, learn the lung region branch using lung region images, which are identified and cropped by the Lung Region Generator(LRG-1). Then, guided by the attention heat map generated from the lung region branch learning, we inference a mask to crop a medical discriminative lesion region from the lung region images by the Lesion Region Generator(LRG-2). The lesion region images are used for training a lesion branch. Lastly, the obtained medical discriminative features knowledge are fused by the feature fusion model for disease classification. We have evaluated the proposed method on the NIH ChestX-ray 14 dataset and achieves the average AUC of 0.878, and the experiment results demonstrate the superiority and effectiveness of the proposed method, compared to other state-of-the-art methods.

Context: Previous studies have shown that a transfer learning model, TCA+ proposed by Nam et al., can significantly improve the performance of cross-project defect prediction (CPDP). TCA+ achieves the improvement by reducing data distribution difference between source (training data) and target (testing data) projects. However, TCA+ is unstable, i.e., its performance varies largely when using different source projects to build prediction models. In practice, it is hard to choose a suitable source project to build the prediction model. Objective: To address the limitation of TCA+, we propose a two-phase transfer learning model (TPTL) for CPDP. Method: In the first phase, we propose a source project estimator (SPE) to automatically choose two source projects with the highest distribution similarity to a target project from candidates. Next, two source projects that are estimated to achieve the highest values of F1-score and cost-effectiveness are selected. In the second phase, we leverage TCA+ to build two prediction models based on the two selected projects and combine their prediction results to further improve the prediction performance. Results: We evaluate TPTL on 42 defect datasets from PROMISE repository, and compare it with two versions of TCA+ (TCA+_Rnd, randomly selecting one source project; TCA+_All, using all alternative source projects), a related source project selection model TDS proposed by Herbold, a state-of-the-art CPDP model leveraging a log transformation (LT) method, and a transfer learning model Dycom with better form of TCA. Experiment results show that, on average across 42 datasets, TPTL respectively improves these baseline models by 19%, 5%, 36%, 27%, and 11% in terms of F1-score; by 64%, 92%, 71%, 11%, and 66% in terms of cost-effectiveness. Conclusion: The proposed TPTL model can solve the instability problem of TCA+, showing substantial improvements over the state-of-the-art and related CPDP models.

Software change-proneness prediction (whether or not class files in a project will be changed in the next release) can help software developers to focus on preventive actions to reduce maintenance costs, and managers to allocate resources more effectively. Prior studies found that change-proneness prediction works well if there is sufficient amount of training data to build a model. However, it is not feasible for projects with limited historical data especially for new projects. To address this issue, cross-project change-proneness prediction, which builds a prediction model by using data in another project (i.e., source project), and predicts the change-proneness in a target project, is proposed. Considering there are a large number of source projects, one challenge for cross-project change-proneness prediction is that given a target project, how to automatically select a source project which could show good prediction accuracy on it. In this paper, we propose a selective cross-project (SCP) model for change-proneness prediction. SCP automatically finds the source project which has the similar data distribution with the target project by measuring distribution similarity between source and target projects. We evaluate SCP by conducting an empirical study on 14 open source projects. We compare it with 2 most related change-proneness models, including RCP (Random Cross-Project prediction) proposed by Malhotra and Bansal, and CLAMI+ developed by Yan et al. Experiment results show that SCP improves RCP and CLAMI+ by 25.34% and 4.30% in terms of AUC respectively; and by 171.42% and 172.31% in terms of cost-effectiveness, respectively.

Residuals and inception are two commonly used module that makes the network deeper and wider to achieve better performance. And the combination of these two modules which is usually referred to as inception-resnet can get a better result. In this paper, we propose a new type of combination to give full play to the role of residuals and inception, making network learning more abundant features. The new proposed module is called Residual Inception (RI) which enjoys the same width as the inception module in GoogLeNet. In RI, each parallel cascade structure is replaced by a densely block or a modified residual block for gaining a better performance and a lower computational cost. Finally, we evaluate our proposed network on three highly competitive datasets and the results demonstrate its superiority in comparison with the state-of-the-art.

As a commonly used data representation technique, Nonnegative Matrix Factorization (NMF) has received extensive attentions in the pattern recognition and machine learning communities over decades, since its working mechanism is in accordance with the way how the human brain recognizes objects. Inspired by the remarkable successes of manifold learning, more and more researchers attempt to incorporate the manifold learning into NMF for finding a compact representation ,which uncovers the hidden semantics and respects the intrinsic geometric structure simultaneously. Graph regularized Nonnegative Matrix Factorization (GNMF) is one of the representative approaches in this category. The core of such approach is the graph, since a good graph can accurately reveal the relations of samples which benefits the data geometric structure depiction. In this paper, we leverage the sparse representation to construct a sparse hypergraph for better capturing the manifold structure of data, and then impose the sparse hypergraph as a regularization to the NMF framework to present a novel GNMF algorithm called Sparse Hypergraph regularized Nonnegative Matrix Factorization (SHNMF). Since the sparse hypergraph inherits the merits of both the sparse representation and the hypergraph model, SHNMF enjoys more robustness and can better exploit the high-order discriminant manifold information for data representation . We apply our work to address the image clustering issue for evaluation. The experimental results on five popular image databases show the promising performances of the proposed approach in comparison with the state-of-the-art NMF algorithms.

The single-feature-based background model often fails in complex scenes, since a pixel is better described by several features, which highlight different characteristics of it. Therefore, the multi-feature-based background model has drawn much attention recently. In this paper, we propose a novel multi-feature-based background model, named stability of adaptive feature (SoAF) model, which utilizes the stabilities of different features in a pixel to adaptively weigh the contributions of these features for foreground detection. We do this mainly due to the fact that the features of pixels in the background are often more stable. In SoAF, a pixel is described by several features and each of these features is depicted by a unimodal model that offers an initial label of the target pixel. Then, we measure the stability of each feature by its histogram statistics over a time sequence and use them as weights to assemble the aforementioned unimodal models to yield the final label. The experiments on some standard benchmarks, which contain the complex scenes, demonstrate that the proposed approach achieves promising performance in comparison with some state-of-the-art approaches.

Static analysis tools (e.g., FindBugs) are widely used to detect potential defects in software development. A recent study suggests that there is a moderate correlation between the alerts reported by static analysis tools and software defects [1]. However, despite the actionable alerts reported by static analysis tools, they may report too many meaningless unactionable alerts. Actionable alert refers to the alert which is meaningful and fixable. Unactionable alert (i.e., false positive alert) refers to the alert which is regarded as unimportant to developers, inessential to source code, or will not be fixed by developers. Are all alerts (including both actionable and unactionable alerts) suitable for indicating software defects? To address this question, we classify all the alerts into two categories, namely actionable alerts and unactionable alerts. By the following, we conduct an empirical study to evaluate the degree of correlation between defects and alerts on the evolution of three open source projects with totally 40 releases. The objective of the study is to explore two kinds of correlation analysis: one is the correlation between all the alerts reported by FindBugs and defects among the release history of a project, the other is the correlation between the actionable alerts and defects. As a result, we find that not all the alerts but the actionable alerts are suitable to be an early predictor of defects.

Background: Software defect models can help software quality assurance teams to allocate testing or code review resources. A variety of techniques have been used to build defect prediction models, including supervised and unsupervised methods. Recently, Yang et al. [1] surprisingly find that unsupervised models can perform statistically significantly better than supervised models in effort-aware change-level defect prediction. However, little is known about relative performance of unsupervised and supervised models for effort-aware file-level defect prediction. Goal: Inspired by their work, we aim to investigate whether a similar finding holds in effort-aware file-level defect prediction. Method: We replicate Yang et al.'s study on PROMISE dataset with totally ten projects. We compare the effectiveness of unsupervised and supervised prediction models for effort-aware file-level defect prediction. Results: We find that the conclusion of Yang et al. [1] does not hold under within-project but holds under cross-project setting for file-level defect prediction. In addition, following the recommendations given by the best unsupervised model, developers needs to inspect statistically significantly more files than that of supervised models considering the same inspection effort (i.e., LOC). Conclusions: (a) Unsupervised models do not perform statistically significantly better than state-of-art supervised model under within-project setting, (b) Unsupervised models can perform statistically significantly better than state-ofart supervised model under cross-project setting, (c) We suggest that not only LOC but also number of files needed to be inspected should be considered when evaluating effort-aware filelevel defect prediction models.

Software Quality model is a well-accepted way for assessing high-level quality characteristics (e.g., maintainability) by aggregation from low-level metrics. Aggregation method in a software quality model denotes how to aggregate low-level metrics to high-level quality characteristics. Most of the existing quality models adopt the weighted linear aggregation method. The main drawback of weighted linear method is that it suffers from a lack of consensus in how to decide the correct weights. To address this issue, we present an automated aggregation method which adopts a kind of probabilistic weight instead of the subjective weight in previous aggregation methods. In particular, we leverage a topic modeling technique to estimate the probabilistic weight by learning from a software benchmark.In this manner, our approach can enable automated quality assessment by using the learned probabilistic relationship without manual effort. To evaluate the effectiveness of proposed aggregation approach, we conduct an empirical study on assessing one typical high-level quality characteristic (i.e., maintainability) which is regarded as an important characteristic defined in ISO 9126. The achieved results on 10 open source projects with totally 269 versions show that our method can reveal maintainability well and it outperforms a weighted linear aggregation method baseline in most of the projects.

Software change-prone class prediction can enhance software decision making activities during software maintenance (e.g., resource allocating).Many change-prone class prediction approaches have been proposed and most are effective in interversion prediction within a project.These approaches usually build a supervised prediction model by learning from historical labeled dataset.However, a major challenge which remains is that this typical change-prone prediction setting cannot be used for new projects or projects with limited historical data.To address this challenge, we propose to tackle this task by adopting a novel prediction method which has not been used in changeprone prediction, namely self-learning method.The key idea of the self-learning method is to enable the change-prone prediction on new projects or projects with limited historical dataset by learning from itself.In this paper, we apply a state-of-art selflearning method, CLAMI, to change-prone prediction.In addition, we propose a novel self-learning approach CLAMI+ by extending CLAMI.The experiments among 14 open source projects show that the self-learning methods achieve comparable results to four typical inter-version baselines and the proposed CLAMI+ slightly improves the CLAMI method on average.

Sparsity Preserving Projections (SPP) is a well known approach for feature extraction and dimensionality reduction. Its success is mainly attributed to its high quality graph which is constructed by sparse representation. As an instance of graph embedding, SPP can be formulated as regression model. Thus we apply the idea of collaborative graph embedding, which reformulates SPP as a collaborative representation model via imposing a L2-norm constraint to projections from the perspective of linear regression, to further enhance SPP. We call this novel SPP method Collaborative Sparsity Preserving Projections (CSPP). Experiment results on four popular face datasets, namely Yale, ORL, FERET and AR, show the effectiveness in feature extraction and the improvement of CSPP over SPP.

The traditional duplicate bug reports detection approaches are usually based on vector space model. However, the experimental result is rarely satisfying since this method cannot distinguish semantic correlation among bug reports which written by natural languages. Topic model, as a method to model underlying topics of texts, can solve the problem of document similarity calculation methods used in the information retrieving. It can find the semantic topics among the texts through massive training data, and obtain semantic relatedness among documents. Therefore, this paper proposes a novel duplication detection method based on topic model. Through selecting bug reports with execution information and combing with classified information of bugs, not only does this new method overcome the problem of high dimension, sparse data and loud noise, but also avoid the problem of synonymy and ambiguity in the natural languages. Comparing to the traditional SVM method, the recall rate and precision rate of our proposed approach have obviously increased, which indicates the effectiveness of this new method.

Programming question and answer (Q&A) websites, such as Stack Overflow, gathered knowledge and expertise of developers from all over the world, this knowledge reflects some insight into the development activities. To comprehend the actual thoughts and needs of the developers, we analyzed the non-functional requirements (NFRs) on Stack Overflow. In this paper, we acquired the textual content of Stack Overflow discussions, utilized the topic model, latent Dirichlet allocation (LDA), to discover the main topics of Stack Overflow discussions, and we used the wordlists to find the relationship between the discussions and NFRs. We focus on the hot and unresolved NFRs, the evolutions and trends of the NFRs in their discussions. We found that the most frequent topics the developers discuss are about usability and reliability while they concern few about maintainability and efficiency. The most unresolved problems also occurred in usability and reliability. Moreover, from the visualization of the NFR evolutions over time, we can find the trend for each NFR.

Scale-space behavior of corners is important for developing an efficient corner detection algorithm. In this paper, we analyze the scale-space behavior with the Laplacian of Gaussian (LoG) operator on a planar curve which constructs Laplacian Scale Space (LSS). The analytical expression of a Laplacian Scale-Space map (LSS map) is obtained, demonstrating the Laplacian Scale-Space behavior of the planar curve corners, based on a newly defined unified corner model. With this formula, some Laplacian Scale-Space behavior is summarized. Although LSS demonstrates some similarities to Curvature Scale Space (CSS), there are still some differences. First, no new extreme points are generated in the LSS. Second, the behavior of different cases of a corner model is consistent and simple. This makes it easy to trace the corner in a scale space. At last, the behavior of LSS is verified in an experiment on a digital curve.

We present a scheme that propagates a reference skeletal model (s-rep) into a particular case of an object, thereby propagating the initial shape-related layout of the skeleton-to-boundary vectors, called spokes. The scheme represents the surfaces of the template as well as the target objects by spherical harmonics and computes a warp between these via a thin plate spline. To form the propagated s-rep, it applies the warp to the spokes of the template s-rep and then statistically refines. This automatic approach promises to make s-rep fitting robust for complicated objects, which allows s-rep based statistics to be available to all. The improvement in fitting and statistics is significant compared with the previous methods and in statistics compared with a state-of-the-art boundary based method.

This study proposes a new method for the detection of local invariant features with contour. This method differs from traditional methods that use image intensity. Image contours can be extracted stably with changes in viewpoint, scale, illumination and other factors. The proposed algorithm first extracts the stable corner from the contour, then it fits the supporting region of the contour near the corner to an angle, and uses its bisector as the direction of the feature. Next, it searches the contour for the tangent point in the direction of the angle bisector. Finally, with the corner as the centre, and in combination with the tangent point and the feature direction, an elliptic invariant region is constructed. The feasibility of the algorithm was verified experimentally by comparing its repetition rate. Test images obtained from actual scenes include several types of transformations, such as rotation, scaling, affinity, illumination and noise. The results of the experiment show the feasibility of the proposed method for use in local invariant features detection.