Bayesian inference for a software reliability model using metrics information.

In this paper, we are concerned with predicting the number of faults N and the time to next failure of a piece of software. Information in the form of software metrics data is used to estimate the prior distribution of N via a Poisson regression model. Given failure time data, and a well known model for software failures, we show how to sample the posterior distribution using Gibbs sampling, as implemented in the package "WinBugs". The approach is illustrated with a practical example

BAYESIAN INFERENCE FOR A SOFTWARE RELIABILITY MODEL USING METRICS INFORMATION.

In this paper, we are concerned with predicting the number of faults N and the time to next failure of a piece of software. Information in the form of software metrics data is used to estimate the prior distribution of N via a Poisson regression model. Given failure time data, and a well known model for software failures, we show how to sample the posterior distribution using Gibbs sampling, as implemented in the package "WinBugs". The approach is illustrated with a practical example.

Bayesian inference for fault based software reliability models given software metrics data

We wish to predict the number of faults N and the time to next failure of a piece of software. Software metrics data are used to estimate the prior mean of N via a Poisson regression model. Given failure time data and a some well known fault based models for interfailure times, we show how to sample the relevant posterior distributions via Gibbs sampling using the package Winbugs. Our approach is illustrated with an example

Rigorously assessing software reliability and safety

This paper summarises the state of the art in the assessment of software reliability and safety ("dependability"), and describes some promising developments. A sound demonstration of very high dependability is still impossible before operation of the software; but research is finding ways to make rigorous assessment increasingly feasible. While refined mathematical techniques cannot take the place of factual knowledge, they can allow the decision-maker to draw more accurate conclusions from the knowledge that is available

A bayesian analysis of beta testing

In this article, we define a model for fault detection during the beta testing phase of a software design project. Given sampled data, we illustrate how to estimate the failure rate and the number of faults in the software using Bayesian statistical methods with various different prior distributions. Secondly, given a suitable cost function, we also show how to optimise the duration of a further test period for each one of the prior distribution structures considered

Uncertainty explicit assessment of off-the-shelf software: A Bayesian approach

Assessment of software COTS components is an essential part of component-based software development. Poorly chosen components may lead to solutions of low quality and that are difficult to maintain. The assessment may be based on incomplete knowledge about the COTS component itself and other aspects (e.g. vendor’s credentials, etc.), which may affect the decision of selecting COTS component(s). We argue in favor of assessment methods in which uncertainty is explicitly represented (‘uncertainty explicit’ methods) using probability distributions. We provide details of a Bayesian model, which can be used to capture the uncertainties in the simultaneous assessment of two attributes, thus, also capturing the dependencies that might exist between them. We also provide empirical data from the use of this method for the assessment of off-the-shelf database servers which illustrate the advantages of ‘uncertainty explicit’ methods over conventional methods of COTS component assessment which assume that at the end of the assessment the values of the attributes become known with certainty

A Bayesian modification to the Jelinski-Moranda software reliability growth model

The Jelinski-Moranda (JM) model for software reliability was examined. It is suggested that a major reason for the poor results given by this model is the poor performance of the maximum likelihood method (ML) of parameter estimation. A reparameterization and Bayesian analysis, involving a slight modelling change, are proposed. It is shown that this new Bayesian-Jelinski-Moranda model (BJM) is mathematically quite tractable, and several metrics of interest to practitioners are obtained. The BJM and JM models are compared by using several sets of real software failure data collected and in all cases the BJM model gives superior reliability predictions. A change in the assumption which underlay both models to present the debugging process more accurately is discussed

Amortising the Cost of Mutation Based Fault Localisation using Statistical Inference

Mutation analysis can effectively capture the dependency between source code and test results. This has been exploited by Mutation Based Fault Localisation (MBFL) techniques. However, MBFL techniques suffer from the need to expend the high cost of mutation analysis after the observation of failures, which may present a challenge for its practical adoption. We introduce SIMFL (Statistical Inference for Mutation-based Fault Localisation), an MBFL technique that allows users to perform the mutation analysis in advance against an earlier version of the system. SIMFL uses mutants as artificial faults and aims to learn the failure patterns among test cases against different locations of mutations. Once a failure is observed, SIMFL requires either almost no or very small additional cost for analysis, depending on the used inference model. An empirical evaluation of SIMFL using 355 faults in Defects4J shows that SIMFL can successfully localise up to 103 faults at the top, and 152 faults within the top five, on par with state-of-the-art alternatives. The cost of mutation analysis can be further reduced by mutation sampling: SIMFL retains over 80% of its localisation accuracy at the top rank when using only 10% of generated mutants, compared to results obtained without sampling