Software reliability prediction : a multi-modelling approach

Many software reliability models are now available to the user who wishes to assess and make predictions about the future reliability of his/her system by using its past failure behaviour. Experience of applying such models to failure data in the past has shown that, to date, there is no one model that will give accurate predictions in all circumstances (i. e., over different data sets). Recent work has thus concentrated on the development of techniquesf or the assessmenot f the accuracyo f predictions madef or the data of interest. One of these techniques also allows the user to improve initially inaccurate predictions via a process of recalibration. The demonstrated success of the recalibration technique suggests that it may be possible to apply fairly simple models, for example nonparametric models, and to achieve reliability predictions which are as accurate as those which could be obtainedf rom more sophisticated models.Here, it is recommended that a "multi-modelling" approach should be taken to the problem of software reliability prediction. That is, a number of parametric and nonparametric models and the recalibration technique should be applied to the failure data from the system of interest and by using the various analysis techniques accurate predictions may be selected for the future failure behaviour of this system, from amongst all the resulting "prediction systems". This thesis gives guidance on how such an approach should be taken and validates the approach by application of these methods to some real software failure data. In order to minimise the effort on the part of the user, the feasibility of automating such selection between prediction systems is also investigated. The general conclusion of this work is that the "multi-modelling" approach suggested is effective, in terms of obtaining, fairly automatically, reliability predictions which can be trusted for each data source. We make recommendations on how to minimise effort on the part of the user of such techniques, by more intelligent choice of initial software reliability models, application of subsequentte chniquesf or improvement in the accuracy of predictions and automatic selection from amongst the available prediction systems. There is evidence here that we could apply a small number of raw models (some parametric and some non-parametric), the recalibration technique, and finally the metapredictor for automatically choosing the best predictions, and the resulting predictions will probably be about the same in accuracy as any single predictor that could have been used

Warranty Data Analysis: A Review

Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA

Statistical modelling of software reliability

During the six-month period from 1 April 1991 to 30 September 1991 the following research papers in statistical modeling of software reliability appeared: (1) A Nonparametric Software Reliability Growth Model; (2) On the Use and the Performance of Software Reliability Growth Models; (3) Research and Development Issues in Software Reliability Engineering; (4) Special Issues on Software; and (5) Software Reliability and Safety

What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)

Agile teams juggle multiple tasks so professionals are often assigned to multiple projects, especially in service organizations that monitor and maintain a large suite of software for a large user base. If we could predict changes in project conditions changes, then managers could better adjust the staff allocated to those projects.This paper builds such a predictor using data from 832 open source and proprietary applications. Using a time series analysis of the last 4 months of issues, we can forecast how many bug reports and enhancement requests will be generated next month. The forecasts made in this way only require a frequency count of this issue reports (and do not require an historical record of bugs found in the project). That is, this kind of predictive model is very easy to deploy within a project. We hence strongly recommend this method for forecasting future issues, enhancements, and bugs in a project.Comment: Accepted to 2018 International Conference on Software Engineering, at the software engineering in practice track. 10 pages, 10 figure

The determination of measures of software reliability

Measurement of software reliability was carried out during the development of data base software for a multi-sensor tracking system. The failure ratio and failure rate were found to be consistent measures. Trend lines could be established from these measurements that provide good visualization of the progress on the job as a whole as well as on individual modules. Over one-half of the observed failures were due to factors associated with the individual run submission rather than with the code proper. Possible application of these findings for line management, project managers, functional management, and regulatory agencies is discussed. Steps for simplifying the measurement process and for use of these data in predicting operational software reliability are outlined

Overview of Remaining Useful Life prediction techniques in Through-life Engineering Services

Through-life Engineering Services (TES) are essential in the manufacture and servicing of complex engineering products. TES improves support services by providing prognosis of run-to-failure and time-to-failure on-demand data for better decision making. The concept of Remaining Useful Life (RUL) is utilised to predict life-span of components (of a service system) with the purpose of minimising catastrophic failure events in both manufacturing and service sectors. The purpose of this paper is to identify failure mechanisms and emphasise the failure events prediction approaches that can effectively reduce uncertainties. It will demonstrate the classification of techniques used in RUL prediction for optimisation of products’ future use based on current products in-service with regards to predictability, availability and reliability. It presents a mapping of degradation mechanisms against techniques for knowledge acquisition with the objective of presenting to designers and manufacturers ways to improve the life-span of components

A nonparametric software reliability growth model

Miller and Sofer have presented a nonparametric method for estimating the failure rate of a software program. The method is based on the complete monotonicity property of the failure rate function, and uses a regression approach to obtain estimates of the current software failure rate. This completely monotone software model is extended. It is shown how it can also provide long-range predictions of future reliability growth. Preliminary testing indicates that the method is competitive with parametric approaches, while being more robust