Debugging‐workflow‐aware software reliability growth analysis

Cinque, Marcello; Cotroneo, Domenico; Pecchia, Antonio; Pietrantuono, Roberto; Russo, Stefano

doi:10.1002/stvr.1638

Cited by 16 publications

(4 citation statements)

References 46 publications

(71 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Growth theory has been also used more recently to derive an empirical characterization of the debugging process as described by Cinque et al ( 2017 ). As an example, the work presented by Nguyen et al ( 2012 ) leverages growth theory to show that the time to repair is influenced by developer expertise and by the specific application context.…”

Section: Related Workmentioning

confidence: 99%

Modeling Performance of Microservices Systems with Growth Theory

Camilli

Russo

2022

Empir Software Eng

View full text Add to dashboard Cite

Context The microservices architectural style is gaining momentum in the IT industry. This style does not guarantee that a target system can continuously meet acceptable performance levels. The ability to study the violations of performance requirements and eventually predict them would help practitioners to tune techniques like dynamic load balancing or horizontal scaling to achieve the resilience property. Objective The goal of this work is to study the violations of performance requirements of microservices through time series analysis and provide practical instruments that can detect resilient and non-resilient microservices and possibly predict their performance behavior. Method We introduce a new method based on growth theory to model the occurrences of violations of performance requirements as a stochastic process. We applied our method to an in-vitro e-commerce benchmark and an in-production real-world telecommunication system. We interpreted the resulting growth models to characterize the microservices in terms of their transient performance behavior. Results Our empirical evaluation shows that, in most of the cases, the non-linear S-shaped growth models capture the occurrences of performance violations of resilient microservices with high accuracy. The bounded nature associated with this models tell that the performance degradation is limited and thus the microservice is able to come back to an acceptable performance level even under changes in the nominal number of concurrent users. We also detect cases where linear models represent a better description. These microservices are not resilient and exhibit constant growth and unbounded performance violations over time. The application of our methodology to a real in-production system identified additional resilience profiles that were not observed in the in-vitro experiments. These profiles show the ability of services to react differently to the same solicitation. We found that when a service is resilient it can either decrease the rate of the violations occurrences in a continuous manner or with repeated attempts (periodical or not). Conclusions We showed that growth theory can be successfully applied to study the occurences of performance violations of in-vitro and in-production real-world systems. Furthermore, the cost of our model calibration heuristics, based on the mathematical expression of the selected non-linear growth models, is limited. We discussed how the resulting models can shed some light on the trend of performance violations and help engineers to spot problematic microservice operations that exhibit performance issues. Thus, meaningful insights from the application of growth theory have been derived to characterize the behavior of (non) resilient microservices operations.

show abstract

Section: Related Workmentioning

confidence: 99%

Modeling Performance of Microservices Systems with Growth Theory

Camilli

Russo

2022

Empir Software Eng

View full text Add to dashboard Cite

show abstract

“…, σ CaseNum }, where CaseNum represents the number of workflow instances in L, and σ l (1 ≤ l ≤ CaseNum) represents the trace of the l th workflow instance. From the preliminary analysis of the process decomposition algorithm, the extended workflow net PN = (P, T; F) is defined on the basis of the workflow net PN = (P, T; F) as follows: (1) P = P; (2) T = T ∪ {t * }; (3)…”

Section: Process Decomposition Algorithm Based On T-invariantmentioning

confidence: 99%

A Software Testing Workflow Analysis Tool Based on the ADCV Method

Mao,

Han,

et al. 2023

Electronics

View full text Add to dashboard Cite

Based on two progressive aspects of the modeling problems in business process management (BPM), (1) in order to address the increasing complexity of user requirements on workflows underlying various BPM application scenarios, a more verifiable fundamental modeling method must be invented; (2) to address the diversification of software testing processes, more formalized advanced modeling technology must also be applied based on the fundamental modeling method. Aiming to address these modeling problems, this paper first proposes an ADCV (acquisition, decomposition, combination, and verification) method that runs through the core management links of four types of business processes (mining, decomposition, recombination, and verification) and then describes the compositional structure of the ADCV method and the design of corresponding algorithms. Then, the software testing workflow is managed and monitored using the method, and the corresponding analysis tool is implemented based on Petri nets. At the same time, the tool is applied to the case processing of the software testing workflow. Specifically, the workflow models are established successively through ADCV during the process of business iteration. Then, the analysis tool developed with the ADCV method, the model–view–controller (MVC) design pattern, and Java Swing technology are applied to instances of the software testing workflow to realize the modeling and management of the testing processes. Thus, the analysis tool can guarantee the accuracy of the parameter estimations of related software reliability growth models (SRGMs) and ultimately improve the quality of software products.

show abstract

“…Software usually needs to undergo a long testing phase before its release to market to guarantee its reliability 1–5 . During the testing phase, software faults may be detected by the debugger and finally corrected.…”

Section: Introductionmentioning

confidence: 99%

“…Software usually needs to undergo a long testing phase before its release to market to guarantee its reliability. [1][2][3][4][5] During the testing phase, software faults may be detected by the debugger and finally corrected. To track the reliability growth of software during testing, numerous software reliability growth models (SRGMs) have been proposed during the past four decades.…”

Section: Introductionmentioning

confidence: 99%

A model of software fault detection and correction processes considering heterogeneous faults

Xie

Hui

Zhai

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

Numerous software reliability growth models (SRGMs) are proposed in the last four decades to track the reliability growth of software during the testing phase. In many existing models, it is assumed that the faults in the software are of the same type. However, this assumption may not be realistic. Software contains different faults and the detection/correction of different faults can require different efforts. In this paper, we propose a more general software reliability model considering heterogeneous faults, where the parameters for the detection time and correction delay of different faults are assumed to be faults specific and allowed to follow arbitrary distributions. Considering different parameters makes the model more flexible and adaptable to different practical software development situations. Some specific models are derived under different assumptions on the distributions of fault detection time, fault correction delay, and the corresponding parameters. Both MLE and LSE are proposed for parameter estimation. Illustrative examples with real data set are studied to compare the proposed models with homogeneous faults model, and the results have shown advantages of our models. The optimal software release policy based on the proposed model is also studied.

show abstract

Debugging‐workflow‐aware software reliability growth analysis

Cited by 16 publications

References 46 publications

Modeling Performance of Microservices Systems with Growth Theory

Modeling Performance of Microservices Systems with Growth Theory

A Software Testing Workflow Analysis Tool Based on the ADCV Method

A model of software fault detection and correction processes considering heterogeneous faults

Contact Info

Product

Resources

About