国产bbaaaaa片,成年美女黄网站色视频免费,成年黄大片,а天堂中文最新一区二区三区,成人精品视频一区二区三区尤物

首頁> 外文學位 >A machine learning-based approach for dynamic reliability assessment of mission critical software systems.
【24h】

A machine learning-based approach for dynamic reliability assessment of mission critical software systems.

機譯：一種基于機器學習的方法，用于關鍵任務軟件系統(tǒng)的動態(tài)可靠性評估。

獲取原文
獲取原文并翻譯 | 示例
  • 摘要
  • 著錄項
  • 相似文獻
  • 相關主題

摘要

Software continues to become more complex and difficult to certify to a high degree of confidence due to the increasing scope and sophistication of the requirements. Consequently, traditional development techniques face growing challenges in satisfying these requirements. Future distributed real-time systems, such as robotic swarm systems, telecontrol systems, and industrial automation systems, may need to dynamically adapt themselves based on the run-time mission-specific requirements and operating conditions. This further compounds the problems of developing highly dependable systems. This is also the case with emerging Service Oriented Architecture (SOA) based systems that perform dynamic discovery of services and reconfiguration and composition of services at run-time. These dynamic features combined with the abstractions provided by the services necessitate the need for high-confidence run-time software reliability assessment techniques.; This Dissertation investigates machine learning-based software defect prediction techniques to monitor and assess the services in the synthesized code. Experimental assessment of various prediction algorithms using real-world data shows that memory-based reasoning (MBR) techniques perform relatively better than other methods. Based on these results, a framework is developed to automatically derive the optimal configuration of an MBR classifier for software defect data by logical variations of its configuration parameters. This adaptive MBR technique provides a flexible and effective environment for accurate prediction of mission-critical software defect data.; In practice, since these systems are dynamically assembled from existing services, a dearth of sufficient sample data regarding the actual operational environment can reduce the level of confidence in the reliability estimate. The Dissertation investigates the combination of Bayesian Belief Network (BBN) and MBR methodologies to integrate multiple evidences from all the services to obtain high-confidence estimates in the reliability of dynamically assembled mission-critical SOA-based systems. Latent defects in more frequently executed domains affect the reliability of the component much more than the domains tested using random testing strategies. A dynamic monitoring and diagnosis framework is developed to accurately estimate the reliability of the system as it executes. The framework incorporates a Markov model to determine the service reliability from its component reliabilities. This systematic assessment method is evaluated using a simulated system and a real-world case study involving an Enterprise Content Management System. An Intelligent Software Defect Analysis Tool (ISDAT) that implements the above framework is developed, to realize the framework objectives of providing a unified framework for dynamically assessing the reliability of mission-critical SOA-based systems to a high-degree of confidence by using AI-based prediction analysis on the defect metrics data collected from real-time system monitoring.
機譯：由于需求范圍的不斷擴大和復雜性的提高，軟件繼續(xù)變得更加復雜且難以高度可信地進行認證。因此，傳統(tǒng)的開發(fā)技術在滿足這些要求方面面臨越來越大的挑戰(zhàn)。未來的分布式實時系統(tǒng)（例如機器人群系統(tǒng)，遠程控制系統(tǒng)和工業(yè)自動化系統(tǒng)）可能需要根據(jù)特定于運行時任務的要求和操作條件動態(tài)地進行自我調整。這進一步加劇了開發(fā)高度可靠的系統(tǒng)的問題。新興的基于服務導向架構（SOA）的系統(tǒng)也是如此，該系統(tǒng)在運行時執(zhí)行服務的動態(tài)發(fā)現(xiàn)以及服務的重新配置和組合。這些動態(tài)功能與服務提供的抽象相結合，因此需要高可信度的運行時軟件可靠性評估技術。本文研究了基于機器學習的軟件缺陷預測技術，以監(jiān)控和評估合成代碼中的服務。使用實際數(shù)據(jù)對各種預測算法進行的實驗評估表明，基于內存的推理（MBR）技術的性能相對優(yōu)于其他方法?；谶@些結果，開發(fā)了一種框架，該框架可通過其配置參數(shù)的邏輯變化自動得出用于軟件缺陷數(shù)據(jù)的MBR分類器的最佳配置。這種自適應MBR技術為靈活預測關鍵任務軟件缺陷數(shù)據(jù)提供了靈活有效的環(huán)境。在實踐中，由于這些系統(tǒng)是從現(xiàn)有服務中動態(tài)組裝而成的，因此缺乏有關實際操作環(huán)境的足夠樣本數(shù)據(jù)可能會降低可靠性估計的可信度。本文研究了貝葉斯信念網(wǎng)絡（BBN）與MBR方法的結合，以整合來自所有服務的多個證據(jù)，從而獲得對基于動態(tài)組裝的關鍵任務SOA的系統(tǒng)的可靠性的高可信度估計。與使用隨機測試策略測試的域相比，執(zhí)行頻率更高的域中的潛在缺陷對組件的可靠性的影響要大得多。開發(fā)了動態(tài)監(jiān)視和診斷框架，以在系統(tǒng)執(zhí)行時準確估計系統(tǒng)的可靠性。該框架包含一個馬爾可夫模型，可從其組件可靠性確定服務可靠性。使用模擬系統(tǒng)和涉及企業(yè)內容管理系統(tǒng)的實際案例研究來評估這種系統(tǒng)的評估方法。開發(fā)了一種實現(xiàn)上述框架的智能軟件缺陷分析工具（ISDAT），以實現(xiàn)提供一個統(tǒng)一框架的框架目標，該框架可通過使用AI高度動態(tài)地評估基于關鍵任務的SOA的系統(tǒng)的可靠性基于實時系統(tǒng)監(jiān)控收集的缺陷度量數(shù)據(jù)的基于預測的分析。

著錄項

相似文獻

  • 外文文獻
  • 中文文獻
  • 專利
獲取原文

客服郵箱：kefu@zhangqiaokeyan.com

京公網(wǎng)安備：11010802029741號 ICP備案號：京ICP備15016152號-6 六維聯(lián)合信息科技 (北京) 有限公司?版權所有

  • 客服微信

  • 服務號