Since traditional approaches to software reliability modeling allow the user to formulate predictions using data from one realization of the debugging process, it is necessary to understand the influence of the fault recovery order on predictive performance. We introduce an experimental methodology using a data structure called the debugging graph and use it to analyze the effects of various fault recovery orders on the predictive accuracy of four well-known sofrware reliability algorithms. Further we note fault interactions and their potential effects on the predictive process. Based on our experiment, we conjecture that the accuracy of a reliability prediction is affected by the fault recovery order as well as by fault interactions.Empirical Studies *This research was partially supported by NASA grant NAG-1-750.stage of the fault removal process. We investigate multiple orders for the fault removal process and isolate the effects of varying the recovery order by using an average of multiple observations to represent each interfailure time.It has been conjectured in the literature that one failure may prevent access to or hide certain others. The manifestation of this has been called the fault interaction phenomenon [3]. Other researchers claim that in practice, although such interactions may occasionally occur during unit testing, they are much less common during system testing or in the operational phase [ 6 ] . Our data display some manifestations of fault interactions.We believe that fault recovery order may affect the accuracy of predictions made by software reliability models, and that the effects of fault interactions are subsumed by the recovery order problem, since for a particular recovery order the context in which each fault contributes to the failure rate is fixed. Here we focus on the use of the debugging graph to analyze the effects of recovery order on reliability predictions. 1: Introduction 2: Terminology and modelsIt has been observed that predicting the reliability of a program has proved to be an unexpectedly challenging task for over two decades [l]. Also, life critical applications of software demand better performance and increased accuracy from reliability models. This has motivated us to investigate sources of inaccuracy in existing models with the hope of improving their performance and/or of discovering better models.Typically software reliability models use a sequence of interfailure times from the debugging process to predict reliability. However the sequence of interfailure times is derived from only one of many possible repair orders. If one assumes data from n failures are being used, then there are n! possible orders in which those faults could have been individually identified and repaired. Also uncertainty about the order of fault recovery is compounded in that a sample of size one is used to represent the interfailure time of the software for each Software reliability (R) is the probability of a software product operating for a given period of time in a particular environment...
Cybersecurity exploits that take advantage of weak passwords continue to succeed in virtually every industry. This motivates interest in empirically determining the extent to which websites that invite visitors to create new user accounts on them encourage or require users to engage in better password management practices, including strong passwords. This project examined a statistically significant sample of websites to assess how closely they voluntarily adhere to the National Institute of Standards and Technology’s authoritative guidance on password policies. Over 100 representative websites were selected from industries that consistently report the most breaches in the Verizon Data Breach Investigation Report. Their respective user account creation processes were assessed via a scorecard approach based on observations collected when following standardized experimental procedures. Scorecard data then were aggregated and analyzed for trends. The research findings highlight potential vulnerabilities that persist in online account password creation practices, leaving many websites susceptible to brute force attacks due to cyber hygiene lapses. Recommendations to help remediate compliance gaps and as paths forward to build upon this work include refining the proposed scorecard, creating and using standardized user registration and profile manager plugins, widely adopting user-friendly password management tools, and enacting tougher legal consequences for website hosts when breaches occur.
Due to the recent pandemic, video conferencing platforms – once niche products aimed at limited communities have become a pervasive way of conducting business and sustaining social connections on a global scale. This project explored cybersecurity vulnerabilities and risks faced by these platforms – their data, hardware, and the information exchanged during virtual meetings – and explains some ways these issues can be mitigated. Published research was compiled and analyzed to uncover general risks, vulnerabilities, and security measures. Then, three popular platforms – Zoom, Skype and GoToMeeting were subjected to closer scrutiny. Findings show that platform vendors, business organizations, education institutions, and end users all bear responsibility to train themselves and their constituents on specific cybersecurity steps to enhance video conferencing security. Targeted recommendations are shared, along with some opportunities to build upon this research in the future.
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