Experimental investigation of memory-related software aging in LLM systems

Balancing accessibility and security: Safeguarding citizen-sourced biodiversity data in the age of AI and open-sourced software

Abstract Context Previous research on software aging is limited, with a focus on dynamic runtime indicators like memory and performance, often neglecting evolutionary indicators like source code comments and narrowly examining legacy issues within the Technical Debt (TD) context. Objective We introduce the concept of Aging Debt (AD), representing the increased maintenance efforts and costs needed to keep software updated. We study AD through Self-Admitted Aging Debt (SAAD) observed in source code comments left by software developers. Method We employ a mixed-methods approach, combining qualitative and quantitative analyses to detect and measure AD in software. This includes framing SAAD patterns from the source code comments after analysing the source code context, then utilizing the SAAD patterns to detect SAAD comments. In the process, we develop a taxonomy for SAAD that reflects the temporal aging of software and its associated debt. Then we utilize the taxonomy to quantify the different types of AD prevalent in Open Source Software (OSS) repositories. Results Our proposed taxonomy categorizes evolutionary software aging into Active and Dormant types. Our extensive analysis of over 9,000+ OSS repositories reveals that more than 21% repositories exhibit signs of SAAD as observed from our gold standard SAAD dataset. Notably, Dormant AD emerges as the predominant category, highlighting a critical but often overlooked aspect of software maintenance. Conclusion As software volume grows annually, so do evolutionary aging and maintenance challenges; our proposed taxonomy can aid researchers in detailed software aging studies and help practitioners develop improved and proactive maintenance strategies.

Container technology, as the key enabler behind microservice architectures, is widely applied in Cloud and Edge Computing. A long and continuous running of operating system (OS) hosting container-based services can encounter software aging that leads to performance deterioration and even causes system failures. OS rejuvenation techniques can mitigate the impact of software aging but the rejuvenation trigger interval needs to be carefully determined to reduce the downtime cost due to rejuvenation. This paper proposes a comprehensive semi-Markovbased approach to quantitatively evaluate the effect of OS rejuvenation on the dependability and the performance of a container-based service. In contrast to the existing studies, we neither restrict the distributions of time intervals of events to be exponential nor assume that backup resources are always available. Through the numerical study, we show the optimal container-migration trigger intervals that can maximize the dependability or minimize the performance of a container-based service.

A Comparative Analysis of Software Aging in Relational Database System Environments

Software Aging Detection and Rejuvenation Assessment in Heterogeneous Virtual Networks

Guest Editorial: Special Section on Applied Software Aging and Rejuvenation

Benchmarking Software Aging Effects in Container Platforms

SGT: Aging-related bug prediction via semantic feature learning based on graph-transformer

PMTT: Parallel multi-scale temporal convolution network and transformer for predicting the time to aging failure of software systems

A novel multi-step-ahead approach for cloud server aging prediction based on hybrid deep learning model

Availability is an important index for the evaluation of the performance of software aging systems. Although the corrective maintenance increases the system availability, the associated cost may be very high; therefore, the balancing of availability and cost during the corrective maintenance phase is a critical issue. This paper investigates optimal corrective maintenance policies via an availability-cost hybrid factor for software aging systems. The system is described by a group of coupled differential equations, where the multiplier effect of the repair rate on a system variable is bilinear term. Our aim is to drive an optimal repair rate that ensures a balance between the maximal system availability and the minimal repair cost. In a finite time interval [0,T], we rigorously discuss the state space of the system and prove the existence of the optimal repair rate, and then derive the first-order necessary optimality conditions by applying a variational inequality with the adjoint variables.

An efficient rejuvenation policy to cope with software aging phenomenon

This paper aims to use analytical modeling technique to quantitatively study the dependability of Vehicle Platooning Application, which consists of Multiple Sub-Services (VPP-MSS) to achieve its functionality. Each sub-service (SS), based on network function virtualization technology, is executed in a container. Both SSes and OSes which SSes run on can suffer from software aging after a long and continuous running, reducing VPP-MSS dependability. Rejuvenation techniques are usually used to combat software aging, but they require the support of backup components. Quantitative study of VPP-MSS dependability enables in-depth understanding of the effectiveness of rejuvenation techniques based on analytical models. In contrast to the existing studies, we develop a semi-Markov process (SMP) model to jointly analyze the impact of rejuvenation technique trigger intervals (RTTIs), backup components' behaviors, time-dependent interactions between various behaviors and the number of active SSes deployed on an OS on the effectiveness of rejuvenation technique. Sensitivity analysis helps identify key parameters for improving the dependability of VPP-MSS. Extensive numerical experiments demonstrate the necessity of considering backup components' behaviors and investigating non-exponentially distributed failure times. We also determine both the optimal RTTI combination and the optimal combination of SSes and OSes, which can maximize VPP-MSS dependability.

When the cloud platform runs under heavy load for a long time, internal resources will be consumed and errors will accumulate continuously. As a result, the software aging phenomenon occurs, which ultimately degrades the performance and reliability of the software system. Aiming at the above problems, this paper proposes a hybrid model based on integrated variational mode decomposition, moving average free regression and long and short memory network (VMD-ARIMA-BILSTM) to predict the software aging problem. Firstly, the original resource utilization rate is decomposed into stationary time series and non-stationary time series by variational mode decomposition. Then, the advantages of moving average free regression and bidirectional long short-term memory network are used to predict stationary and non-stationary series respectively. Finally, the prediction results are reconstructed to obtain the final prediction results. Experimental results show that compared with single ARIMA and BI-LSTM, the hybrid model designed in this paper has higher prediction accuracy and faster convergence speed.

Software aging is the process caused by Aging-Related Bugs (ARBs) which leads to the depletion of resources and degradation of performance in the long run. ARBs are difficult to find and replicate in future studies as they are less in number, thus prediction of ARB is necessary to save cost and time in the testing phase. ARBs are present in low proportion as compared to non-ARBs known as the class Imbalance problem resulting in insufficient training dataset for prediction models. In this study, Synthetic Minority Oversampling Technique (SMOTE) is applied along with homogeneous cross-project ARB prediction to reduce the effect of imbalance problem in software. SMOTE is oversampling of the minority instances synthetically to balance the dataset and improve the capability of defect prediction models. Homogeneous cross-project prediction is implemented where the datasets are different but the distribution of metric sets of both training and testing datasets is similar. The experiment is conducted on five cloud-oriented software such as Cassandra, Hive, Storm, Hadoop HDFS and Hadoop Mapreduce. The novelty of this study is the combination of SMOTE and homogeneous cross-project defect prediction for ARBs in cloud-oriented software. The comparative analysis is also conducted to understand the difference between SMOTE and non-SMOTE results with the help of machine learning classifiers. The result conveys that SMOTE is an efficient method to address class imbalance problem in ARB prediction.

Constance computer program changes during software maintenance cause program structure and its execution to degrade which in the long run reduce the quality of the program, driving to the rise of software ageing. Change analysis is essential to assess and oversee the effect of changes to handle ageing issue. However, the evaluation of risks is still vague. There are also insufficient tools for change analysis amid software maintenance for ensuring anti-ageing. This motivates the study to propose risk mitigation process as a method to evaluate the risks from software change and construct a prototype as a platform to aid change analysis phase during software maintenance. The prototype aims to foresee and minimize chances of risks from software changes that lead to software ageing. This paper discusses on the development of a prototype named Risk Mitigation for Software Anti-Ageing System that is designed and developed based on software development lifecycle methodology to establish successful development and implementation of the system. The discoveries from this study offer assistance for software maintainers to conduct risk assessment during change analysis in software maintenance via the digital risk mitigation process to ensure software anti-ageing.

Software aging, a phenomenon that exhibits an increasing failure rate or progressive performance degradation in long-running software systems, has caused serious cost damage or even loss of human lives. To aid aging-related bug (ARB, whose activation can result in software aging) detection and removal before software release, ARB prediction was proposed. Based on the prediction results, software teams can allocate limited testing resources to ARB-prone modules. Previous research has proposed several methods for both within-project and cross-project ARB prediction. However, they are based on the same set of metrics focusing on the contents of a single module, and only six metrics are aging-related. In this paper, we develop aging-related network measures by constructing an aging-related dependency network to model the flow of aging-related information in the software. Our evaluation on three commonly used open-source projects reveals that aging-related network measures show an inconsistent association with ARB-proneness in three projects, and the performance of aging-related network measures varies under different ARB prediction settings.

Software aging, which is caused by the accumulation of errors in the system and the consumption of computing resources, tends to occur in long-running cloud service software systems. In practice, software aging prediction has proven to be useful in planning the time to trigger rejuvenation because it provides a prior estimate of future resource consumption. However, aging indicators (e.g., physical memory) in cloud may have the characteristics of long-term slow growth, medium-term seasonality variations (alternating peaks and troughs), and short-term irregular fluctuations. Unfortunately, most of the existing aging prediction methods (e.g., a statistical or single machine learning model) only focus on the accuracy of short-term prediction, while lacking the cognition of the medium and long-term variations of aging indicators and their functions in formulating the rejuvenation schedule (e.g., performing rejuvenation when the load is low can minimize interference to users). To address the above problems, this paper proposes a novel hybrid aging prediction framework to work on the prediction of memory resource consumption in cloud by applying a seasonal-trend decomposition procedure based on loess (STL) method and Gate Recurrent Unit (GRU) neural network, called the decomposition-based GRU (DGRU). The effectiveness of DGRU mainly has two aspects. One is the STL method as a preprocessing technology that can extract the trend, seasonality, and residual characteristics from memory utilization data. The other is that these characteristics can be well predicted separately by a well-designed GRU model, which can model the time-series relationship between the data. Experimental results show that our DGRU framework has superior performance compared with its competitors, including seven single models and six hybrid models. Our study illustrates that the DGRU is a promising solution for high-precision software aging prediction.

The Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) integrated architecture is a key enabling platform for 5G to run multiple customized services in the form of service function chain (SFC) configured as an ordered set of service functions (SFs). However, memory-related software aging in the SF that can be exploited by attackers becomes a new threat to the dependability of MEC-SFC services. To provide dependable MEC-SFC services, proactive rejuvenation techniques to counteract the SF aging problem are essential. In this paper, we develop a semi-Markov model to quantitatively investigate the transient availability and steady-state dependability (availability and reliability) of MEC-SFC services. Our model enables the analysis of a MEC-SFC with any number of SFs, and can capture complex time-dependent behaviors of aging, failure, and recovery. The approximate accuracies of the presented model on dependability measures are comprehensively evaluated through comparative studies with simulation experiments. We then detect potential bottlenecks for a MEC-SFC system through sensitivity analysis and further analyze the impact of event-time interval distributions on steady-state dependability. Finally, we investigate the transient behaviors of a MEC-SFC service when varying system parameters during MEC-SFC operation.