A Game Theoretic Approach for Quality Assurance in Software Systems Using Antifragility-Based Learning Hooks

2022 ◽  
pp. 1701-1719
Author(s):  
Vimaladevi M. ◽  
Zayaraz G.
Keyword(s):  
Quality Assurance ◽  
Defect Density ◽  
Fault Injection ◽  
Object Oriented ◽  
Quality Parameters ◽  
Software Systems ◽  
Software System ◽  
Theoretic Approach ◽  
Reward Function ◽  
Learning Reinforcement

The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.

scholarly journals A Game Theoretic Approach for Quality Assurance in Software Systems Using Antifragility-Based Learning Hooks

2020 ◽  
Vol 22 (3) ◽  
pp. 1-18
Author(s):  
Vimaladevi M. ◽  
Zayaraz G.
Keyword(s):  
Quality Assurance ◽  
Defect Density ◽  
Fault Injection ◽  
Object Oriented ◽  
Quality Parameters ◽  
Software Systems ◽  
Software System ◽  
Theoretic Approach ◽  
Reward Function ◽  
Learning Reinforcement

The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.

Test Focus Selection for Integration Testing

2017 ◽  
Vol 27 (08) ◽  
pp. 1145-1166
Author(s):  
Shadi Banitaan ◽  
Kendall E. Nygard ◽  
Kenneth Magel
Keyword(s):  
Object Oriented ◽  
Experimental Results ◽  
Software Systems ◽  
Budget Constraints ◽  
Unit Testing ◽  
Software System ◽  
Integration Testing ◽  
System Testing ◽  
New Approach ◽  
Selection For

Object-oriented software systems contain large number of modules which make unit testing, integration testing, and system testing very difficult and challenging. While the aim of unit testing is to show that individual modules are working properly and the aim of the system testing is to determine whether the whole system meets its specifications, the aim of integration testing is to uncover errors in the interactions between system modules. However, it is generally impossible to test all connections between modules because of time and budget constraints. Thus, it is important to focus the testing on the connections presumed to be more error-prone. The goal of this work is to guide software testers where in a software system to focus when performing integration testing to save time and resources. This paper proposes a new approach to predict and rank error-prone connections. We use method level metrics that capture both dependencies and internal complexity of methods. We performed experiments on several Java applications and used error seeding techniques for evaluation. The experimental results showed that our approach is effective for selecting the test focus in integration testing.

scholarly journals Integrating Production Planning with Truck-Dispatching Decisions through Reinforcement Learning While Managing Uncertainty

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 587
Author(s):  
Joao Pedro de Carvalho ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Reinforcement Learning ◽  
Discrete Event ◽  
Mining Operations ◽  
Fixed Sequence ◽  
Q Learning ◽  
Reward Function ◽  
Copper Gold ◽  
Mining Complex ◽  
Learning Reinforcement ◽  
Operational Plan

This paper presents a new truck dispatching policy approach that is adaptive given different mining complex configurations in order to deliver supply material extracted by the shovels to the processors. The method aims to improve adherence to the operational plan and fleet utilization in a mining complex context. Several sources of operational uncertainty arising from the loading, hauling and dumping activities can influence the dispatching strategy. Given a fixed sequence of extraction of the mining blocks provided by the short-term plan, a discrete event simulator model emulates the interaction arising from these mining operations. The continuous repetition of this simulator and a reward function, associating a score value to each dispatching decision, generate sample experiences to train a deep Q-learning reinforcement learning model. The model learns from past dispatching experience, such that when a new task is required, a well-informed decision can be quickly taken. The approach is tested at a copper–gold mining complex, characterized by uncertainties in equipment performance and geological attributes, and the results show improvements in terms of production targets, metal production, and fleet management.

A documentation scheme for object-oriented software systems

1993 ◽  
Vol 4 (3) ◽  
pp. 6-17 ◽  
Author(s):  
J. Sametinger ◽  
A. Stritzinger
Keyword(s):  
Object Oriented ◽  
Software Systems

scholarly journals A Bayesian Network Based Method for Service Quality Optimization

2018 ◽  
Vol 11 (3) ◽  
pp. 1
Author(s):  
Lian Gaofeng
Keyword(s):  
Quality Assurance ◽  
Service Quality ◽  
Bayesian Network ◽  
Video Quality ◽  
Quality Parameters ◽  
Industrial Sectors ◽  
Internet Streaming ◽  
Contextual Awareness ◽  
The Relationship ◽  
Quality Assurance Method

Video conference, as an application of Internet streaming media, has attracted wide attention from both academic and industrial sectors. However, usersmay encounter many problemsindailyuse, such as poor video quality, playback delay, and lack of adjustable context, whichcausenegative impactson customers’usage experience. Existing end-to-end service quality assurance method mainly analyzes the relationship between the target service quality parameters and the context in a “single” manner. In this paper, we propose a Bayesian network-based service quality assurance method (named as Comprehensively Context-Aware approach, CCA), which combines Bayesian network and fuzzy set theoryand obtainsrandomrelationshipsamongdifferent service quality parameters through contextual awareness. Comprehensive experimentsclearly validate the superiority of CCA against other well-established methods.

A Comprehensive Assessment Tool for Patient Registry Software Systems: The CIPROS Checklist

2015 ◽  
Vol 54 (05) ◽  
pp. 447-454 ◽  
Author(s):  
U. Mansmann ◽  
D. Lindoerfer
Keyword(s):  
Systematic Review ◽  
Critical Appraisal ◽  
Assessment Tool ◽  
Wide Spectrum ◽  
Patient Registry ◽  
Software Systems ◽  
Software System ◽  
Review Of The Literature ◽  
Patient Registries ◽  
Wide Range

SummaryBackground: Patient registries are an important instrument in medical research. Often their structure is complex and their implementation uses composite software systems to meet the wide spectrum of challenges.Objectives: For the implementation of a registry, there is a wide range of commercial, open source, and self-developed systems available and a minimal standard for the critical appraisal of their architecture is needed.Methods: We performed a systematic review of the literature to define a catalogue of relevant criteria to construct a minimal appraisal standard.Results: The CIPROS list is developed based on 64 papers which were found by our systematic review. The list covers twelve sections and contains 72 items.Conclusions: The CIPROS list supports developers to assess requirements on existing systems and strengthens the reporting of patient registry software system descriptions. It can be a first step to create standards for patient registry software system assessments.

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