Plant cells expand and elongate. Their cumulative actuation defines organ morphing. Inspired by this modular transformability, this study proposes a modular concept for growing robots that will be able to grow by adding at their tip Transformable Modules (TMs). We provide a two-module implementation to evaluate the concept viability. We designed and characterized Shape-Retention Bellows (SRBs) that constitute the TM and are used to maintain the shape once the extension force is relaxed. We demonstrate module radial expansion and axial elongation in a straight and bent configuration (up to ~4°). This is the first concept of growing robots to enact the robot's modularity and transformability for future deployment in distributed growing systems capable of acting in various scenarios.

Code summarization is pivotal in software development, aiding developers in grasping the semantics of source code. However, existing research predominantly focuses on the general code summarization capabilities of models, neglecting project-specific summary characteristics. However, given the scarcity of project-internal code summary corpora, enhancing the model’s performance for a specific project presents a significant challenge. To tackle this issue, we introduces the use of In-and-Cross project pseudo-summaries to improve Project-Specific Code Summarization. Specifically, we employ models trained on other projects to generate cross-project pseudo-summaries and learn the distinctions from target-project through contrastive learning. Simultaneously, we utilize in-project pseudo-summaries generated by the current model, harnessing these data through semi-supervised learning to enhance performance. The experiment results show that the proposed method can effectively improve the performance of the summarization task in practical scenarios, and can also enhance the coordination of the model.

In software development, testing is critical in guaranteeing software quality, with test case design at the core of the testing phase. However, generating effective test cases requires deep expertise and significant time and effort. Therefore, much prior research has turned to methods of Natural Language Processing, utilizing generative deep learning methods to automate test case generation. These earlier studies, however, have largely ignored the crucial role of conditional statements within software requirements - a factor we believe is indispensable for generating high-quality test cases. To bridge this gap, we introduce a pioneering approach for automatically deriving antecedents and consequents from requirements, termed as Traceable Sequence Generation (TSG). The TSG generates conditional statements first and then generates corresponding test cases by constructing a Cause-Effect-Graph. To verify the effectiveness of TSG, we constructed a requirement-to-test-case dataset, called Code Test Case Eval (CTCE). The dataset also includes annotated conditional statements for each segment of the requirement text, so we can utilize them to improve test case generation easily. Our experimental results indicate that TSG notably surpasses traditional and NLP-based methods, excelling in conditional statement extraction and generating high-coverage test cases.

Links between issue reports and corresponding code commits to fix them can greatly reduce the maintenance costs of a software project. More often than not, however, these links are missing and thus cannot be fully utilized by developers. Current practices in issue-commit link recovery extract text features and code features in terms of textual similarity from issue reports and commit logs to train their models. These approaches are limited since semantic information could be lost. Furthermore, few of them consider the effect of source code files related to a commit on issue-commit link recovery, let alone the semantics of code context. To tackle these problems, we propose to construct code knowledge graph of a code repository and generate embeddings of source code files to capture the semantics of code context. We also use embeddings to capture the semantics of issue- or commit-related text. Then we use these embeddings to calculate semantic similarity and code similarity using a deep learning approach before training a SVM binary classification model with additional features. Evaluations on real-world projects show that our approach DeepLink can outperform the state-of-the-art method.

Malicious user behavior that does not trigger access violation or data leak alert is difficult to detect.Using the stolen login credentials, the intruder doing espionage will first try to stay undetected, silently collect data that he is authorized to access from the company network.This paper presents an overview of User Behavior Analytics Platform built to collect logs, extract features and detect anomalous users which may contain potential insider threats.Besides, a multi-algorithms ensemble, combining OCSVM, RNN and Isolation Forest, is introduced.The experiment showed that the system with an ensemble of unsupervised anomaly detection algorithms can detect abnormal user behavior patterns.The experiment results indicate that OCSVM and RNN suffer from anomalies in the training set, and iF orest gives more false positives and false negatives, while the ensemble of three algorithms has great performance and achieves recall 96.55% and accuracy 91.24% on average.

An intruder of a company’s network may use stolen login credentials to silently collect sensitive data. Such malicious user behavior is difficult to detect as long as it does not trigger access violation or data leak alert. In this paper, we propose to use an ensemble of three unsupervised anomaly detection algorithms, namely OCSVM, RNN and Isolation Forest, to detect abnormal user behavior patterns. Besides, an User Behavior Analytics (UBA) Platform is proposed to collect logs, extract features and conduct experiments. The experiment results indicate that our algorithm outperforms each individual algorithm with recall of 96.55% and precision of 91.24% on average, while both OCSVM and RNN suffer from anomalies in the training set, and [Formula: see text] produces more false positives and false negatives in prediction.

Hardware Trojan attack in the form of malicious modification of a design has emerged as a major security threat. Sidechannel analysis has been investigated as an alternative to conventional logic testing to detect the presence of hardware Trojans. However, these techniques suffer from decreased sensitivity toward small Trojans, especially because of the large process variations present in modern nanometer technologies. In this paper, we propose a novel noninvasive, multiple-parameter side-channel analysisbased Trojan detection approach. We use the intrinsic relationship between dynamic current and maximum operating frequency of a circuit to isolate the effect of a Trojan circuit from process noise. We propose a vector generation approach and several design/test techniques to improve the detection sensitivity. Simulation results with two large circuits, a 32-bit integer execution unit (IEU) and a 128-bit advanced encryption standard (AES) cipher, show a detection resolution of 1.12 percent amidst ±20 percent parameter variations. The approach is also validated with experimental results. Finally, the use of a combined side-channel analysis and logic testing approach is shown to provide high overall detection coverage for hardware Trojan circuits of varying types and sizes.