SELink: A semantic-enhanced modular framework for issue–commit link recovery

MPLinker: Multi-template Prompt-tuning with adversarial training for Issue–commit Link recovery

To address the problems of poor applicability of existing fault link recovery algorithms in network topology migration and backup path congestion, this paper proposes a backup path algorithm based on graph convolutional neural to improve deep deterministic policy gradient. First, the BPDM-GCN backup path algorithm is constructed within a deep deterministic policy gradient training framework. It uses graph convolutional networks to detect changes in network topology, aiming to optimize data transmission delay and bandwidth occupancy within the network topology. After iterative training of the BPDM-GCN algorithm, the comprehensive link weights within the network topology are generated. Then, according to the comprehensive link weight and taking the shortest path as the optimization objective, a backup path implementation method based on the incremental shortest path tree is designed to reduce the phasor data transmission delay in the backup path. In conclusion, the experimental results show that the backup path formulated by this algorithm exhibits reduced data transmission delay, minimal path extension, and a high success rate in recovering failed links. Compared to the superior NRLF-RL algorithm, the BPDM-GCN algorithm achieves a reduction of approximately 14.29% in the average failure link recovery delay and an increase of approximately 5.24% in the failure link recovery success rate.

Acceleration of Optical Link Recovery for Optical-Switched Data Center Networks via Clock and Protocol Co-Optimization

Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in wireless ad hoc networks (WAHNs) is forwarding-based and therefore more efficient, with lower overheads. However, the packet delivery ratio (PDR) and link recovery time for AODV performs worse compared to flooding-based BLE protocols when encountering link disruptions. We propose the Multipath Optimized AODV (M-O-AODV) protocol to address these issues, with improved PDR and link robustness compared with other forwarding-based protocols. In addition, M-O-AODV achieved a PDR of 88%, comparable to the PDR of 92% for flooding-based BLE, unlike protocols such as Reverse-AODV (R-AODV). Also, M-O-AODV was able to perform link recovery within 3700 ms in the case of node failures, compared with other forwarding-based protocols that require 4800 ms to 6000 ms. Consequently, M-O-AODV-based BLE mesh networks are more efficient for wireless sensor-enabled IoT environments.

An empirical study on the state-of-the-art methods for requirement-to-code traceability link recovery

Deep semi-supervised learning for recovering traceability links between issues and commits

MTLink: Adaptive multi-task learning based pre-trained language model for traceability link recovery between issues and commits

The mobility of low Earth orbit (LEO) satellites causes the LEO satellite network to experience topology changes. Topology change includes periodic topology change that occurs naturally and unpredictable topology change that occurs due to instability of the inter-satellite link between satellites. Periodic and unpredictable topology change causes frequent topology change, requiring massive communications throughout the network due to frequent route convergence. LEO satellites have limited onboard power because they operate on batteries. The waste of limited satellite onboard resources shortens the lifespan of the satellite, and achieving stable end-to-end transmission is challenging for the network. In this regard, minimizing communication overhead is a fundamental consideration when designing a routing scheme. In this paper, we propose a distributed detour routing scheme with minimal communication overhead. This routing scheme consists of a rapid detour, selective flooding, and link recovery procedures. When a link failure occurs in the network, a rapid detour can detect link failure using only a precalculated routing table. Subsequently, selective flooding searches for the optimal detour point within the minimum hop region and flood to detour point. After link recovery, a procedure is defined to traverse the pre-detour path and switch it back to the original path. The simulation results show that the proposed routing scheme achieves a reduction of communication overhead by 97.6% compared with the n-hop flooding approach.

FFRLI: Fast fault recovery scheme based on link importance for data plane in SDN

We propose an analysis of the recovery strategies for the science mode of the Laser Interfer-ometer Space Antenna (LISA) mission after a meteoroid impacts the spacecraft. The mission consists of a three-spacecraft constellation traveling in a heliocentric orbit, detecting gravitational waves through laser interferometry. To this end, each spacecraft must travel in a free-fall condition in order to reject any possible disturbance and noise affecting the control loop. Nevertheless, if one of the three satellites crosses a meteoroids stream, the collisions can produce attitude perturbations that must be compensated by the control loop. Indeed, in this latter case, the interferometer laser links can be lost. Unfortunately, the link recovery is accomplished through a quite time-consuming re-acquisition maneuver, implying a significant reduction of the science mode time. For this reason, we propose different strategies for a fast recovery of the nominal attitude. The strategies are supported and traded-off by means of extensive simulations, including a Monte Carlo campaign and a worst-case analysis.

BTLink : automatic link recovery between issues and commits based on pre-trained BERT model

Requirement traceability links are an essential part of requirement management software and are a basic prerequisite for software artifact changes. The manual establishment of requirement traceability links is time-consuming. When faced with large projects, requirement managers spend a lot of time in establishing relationships from numerous requirements and codes. However, existing techniques for automatic requirement traceability link recovery are limited by the semantic disparity between natural language and programming language, resulting in many methods being less accurate. In this paper, we propose a fine-grained requirement-code traceability link recovery approach based on query expansion, which analyzes the semantic similarity between requirements and codes from a fine-grained perspective, and uses a query expansion technique to establish valid links that deviate from the query, so as to further improve the accuracy of traceability link recovery. Experiments showed that the approach proposed in this paper outperforms state-of-the-art unsupervised traceability link recovery methods, not only specifying the obvious advantages of fine-grained structure analysis for word embedding-based traceability link recovery, but also improving the accuracy of establishing requirement traceability links. The experimental results demonstrate the superiority of our approach.

In industry, software projects might span over decades, with many engineers joining or leaving the company over time. In these circumstances, no single engineer has all of the knowledge when maintenance tasks such as Traceability Link Recovery (TLR), Bug Localization (BL), and Feature Location (FL) are performed. Thus, collaboration has the potential to boost the quality of maintenance tasks since the solution advanced by one engineer might be enhanced with contributions from other engineers. However, assembling a team of software engineers to collaborate may not be as intuitive as we might think. In the context of a worldwide industrial supplier of railway solutions, this work evaluates how the quality of TLR, BL, and FL is affected by the criteria for selecting engineers for collaboration. The criteria for collaboration are based on engineers’ profile information to select the set of search queries that are involved in the maintenance task. Collaboration is achieved by applying automatic query reformulation, and the location relies on an evolutionary algorithm. Our work uncovers how software engineers who might be seen as not being relevant in the collaboration can lead to significantly better results. A focus group confirmed the relevance of the findings.

Network-structured data becomes ubiquitous in daily life and is growing at a rapid pace. It presents great challenges to feature engineering due to the high non-linearity and sparsity of the data. The local and global structure of the real-world networks can be reflected by dynamical transfer behaviors among nodes. This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models. We first design a degree-weight biased random walk model to capture the transfer behaviors on the network. Then a deep network embedding method is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into conventional deep prediction models, including Long Short-Term Memory Network and Recurrent Neural Network, to utilize the sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various datasets including social networks, citation networks, biomedical network, collaboration network and language network. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization, classification, reconstruction and link recovery, and achieve promising performance compared with state-of-the-arts.

When the distribution network is affected by natural disasters or deliberately damaged, failure may cause a large area power outage, which will seriously impact the national economy, social stability, people’s normal life, etc. This paper proposes a composite fault recovery method for the distribution networks based on the maximum-flow method. A service flow model, an information link recovery model, and a multi-objective optimization model are established to effectively solve communication power’s composite fault recovery problem in the distribution network.

Links between pull request and the issues they address document and accelerate the development of a software project but are often omitted. We present a new tool, Aide-mémoire, to suggest such links when a developer submits a pull request or closes an issue, smoothly integrating into existing workflows. In contrast to previous state-of-the-art approaches that repair related commit histories, Aide-mémoire is designed for continuous, real-time, and long-term use, employing Mondrian forest to adapt over a project’s lifetime and continuously improve traceability. Aide-mémoire is tailored for two specific instances of the general traceability problem—namely, commit to issue and pull request to issue links, with a focus on the latter—and exploits data inherent to these two problems to outperform tools for general purpose link recovery. Our approach is online, language-agnostic, and scalable. We evaluate over a corpus of 213 projects and six programming languages, achieving a mean average precision of 0.95. Adopting Aide-mémoire is both efficient and effective: A programmer need only evaluate a single suggested link 94% of the time, and 16% of all discovered links were originally missed by developers.

MANETs are networks of mobile nodes that are randomly distributed and play a major role in data transmission, route discovery, and route maintenance. Connection recovery and path stability are significant issues in the MANET, making data transfer problematic. When mobile nodes move out of range or when the node lacks adequate energy to maintain connection or path stability, data packet loss happens. We suggest a Clustered Intermediate System-Clustered Intermediate System Local Connection Failure Recovery Algorithm (Clustered Intermediate System-Clustered Intermediate System LLFRA) routing migration technique that builds the node while simultaneously repairing the broken link. Also utilised to look at node energy, energy- drained nodes, and route stability is Dolphin Partner Optimisation (DPO). The suggested approach's primary goal is to guarantee route stability while reducing packet loss. The suggested method is put to the test using the NS-2 simulator. The proposed recovery protocol beats the current technique in charge of PDR, end to end delay, throughput and node failures, according to experimental outputs.

Traceability information can be crucial for software maintenance, testing, automatic program repair, and various other software engineering tasks. Customarily, a vast amount of test code is created for systems to maintain and improve software quality. Today's test systems may contain tens of thousands of tests. Finding the parts of code tested by each test case is usually a difficult and time-consuming task without the help of the authors of the tests or at least clear naming conventions. Recent test-to-code traceability research has employed various approaches but textual methods as standalone techniques were investigated only marginally. The naming convention approach is a well-regarded method among developers. Besides their often only voluntary use, however, one of its main weaknesses is that it can only identify one-to-one links. With the use of more versatile text-based methods, candidates could be ranked by similarity, thus producing a number of possible connections. Textual methods also have their disadvantages, even machine learning techniques can only provide semantically connected links from the text itself, these can be refined with the incorporation of structural information. In this paper, we investigate the applicability of three text-based methods both as a standalone traceability link recovery technique and regarding their combination possibilities with each other and with naming conventions. The paper presents an extensive evaluation of these techniques using several source code representations and meta-parameter settings on eight real, medium-sized software systems with a combined size of over 1.25 million lines of code. Our results suggest that with suitable settings, text-based approaches can be used for test-to-code traceability purposes, even where naming conventions were not followed.

In the software development process, traceability links between unit tests and code are not explicitly maintained, and dependencies in most cases are manually identified. As a result, a large amount of effort and time is required during the comprehension process to establish the links between these artifacts. Although there are several methods that can infer such links based on different phenomenons, these methods usually produce different set of traceability links. This work expands upon previous traceability link recovery and visualization studies by implementing a combination of traceability recovery methods that automatically retrieve the links, and visualizing them to help developers to overview the links inferred by various recovery techniques, and also to select the right relations for analyses. The results of the usability study show that the visualization model presented herein can effectively support browsing, comprehension, and maintenance of Test-to Code Traceability (TCT) links in a system with enhanced efficiency, as well as visualization of TCT links from multiple sources is better than a visualization of single source of traceability links.