A practical software-reliability measurement framework based on failure data

This paper presents a method proposal for estimation of software reliability before the implementation phase. The method is based upon that a formal specification technique is used and that it is possible to develop a tool performing dynamic analysis, i.e., locating semantic faults in the design. The analysis is performed with both applying a usage profile as input as well as doing a full analysis, i.e., locate all faults that the tool can find. The tool must provide failure data in terms of time since the last failure was detected. The mapping of the dynamic failures to the failures encountered during statistical usage testing and operation is discussed. The method can be applied either on the software specification or as a step in the development process by applying it on the software design. The proposed method allows for software reliability estimations that can be used both as a quality indicator, and for planning and controlling resources, development times, etc. at an early stage in the development of software systems.

Download Full-text

A SOFTWARE RELIABILITY MODEL FOR ARTIFICIAL INTELLIGENCE PROGRAMS

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194093000057 ◽

1993 ◽

Vol 03 (01) ◽

pp. 99-114 ◽

Cited By ~ 5

Author(s):

FAROKH B. BASTANI ◽

ING-RAY CHEN ◽

TA-WEI TSAO

Keyword(s):

Artificial Intelligence ◽

Software Reliability ◽

Planning Problem ◽

Reliability Growth ◽

Reliability Model ◽

Reliability Models ◽

Failure Data ◽

Software Reliability Models ◽

Software Reliability Model ◽

Correctness Criterion

In this paper we develop a software reliability model for Artificial Intelligence (AI) programs. We show that conventional software reliability models must be modified to incorporate certain special characteristics of AI programs, such as (1) failures due to intrinsic faults, e.g., limitations due to heuristics and other basic AI techniques, (2) fuzzy correctness criterion, i.e., difficulty in accurately classifying the output of some AI programs as correct or incorrect, (3) planning-time versus execution-time tradeoffs, and (4) reliability growth due to an evolving knowledge base. We illustrate the approach by modifying the Musa-Okumoto software reliability growth model to incorporate failures due to intrinsic faults and to accept fuzzy failure data. The utility of the model is exemplified with a robot path-planning problem.

Download Full-text

Highly Reliable Software Reliability Assessment Based on Statistics of Extremes and Bootstrapping Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.670-671.1477 ◽

2014 ◽

Vol 670-671 ◽

pp. 1477-1481

Author(s):

Hao Nan Tong ◽

Qiu Ying Li

Keyword(s):

Software Reliability ◽

Reliability Assessment ◽

Time To Failure ◽

Reliability Engineering ◽

Statistics Of Extremes ◽

Failure Data ◽

Distribution Parameters ◽

Extreme Distribution ◽

Reliable Software ◽

Bootstrapping Method

Reliability assessment of Highly Reliable Software is significant in the software reliability engineering because of the small-size failure data. A novel model based on bootstrapping method and statistics of extremes for highly reliable software reliability assessment was presented. Correlation coefficient method was applied in order to determine the extreme distribution pattern to which the failure data belongs. The bootstrapping method based on residual error was used to estimate the parent distribution parameters. Software reliability and mean-time-to-failure (MTTF) at the end of reliability test were assessed. Experimental results show the model has a higher accuracy in the small-size sample situation. The validity of the proposed method is examined.

Download Full-text

BAYESIAN SOFTWARE RELIABILITY MODEL COMBINING TWO PRIORS AND PREDICTING TOTAL NUMBER OF FAILURES AND FAILURE TIME

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539314500314 ◽

2014 ◽

Vol 21 (06) ◽

pp. 1450031 ◽

Cited By ~ 1

Author(s):

D. DAMODARAN ◽

B. RAVIKUMAR ◽

VELIMUTHU RAMACHANDRAN

Keyword(s):

Bayesian Approach ◽

Software Reliability ◽

Failure Time ◽

Reliability Model ◽

Software Failure ◽

Failure Times ◽

Failure Data ◽

Time Between Failures ◽

Software Reliability Model ◽

Proposed Model

Reliability statistics is divided into two mutually exclusive camps and they are Bayesian and Classical. The classical statistician believes that all distribution parameters are fixed values whereas Bayesians believe that parameters are random variables and have a distribution of their own. Bayesian approach has been applied for the Software Failure data and as a result of that several Bayesian Software Reliability Models have been formulated for the last three decades. A Bayesian approach to software reliability measurement was taken by Littlewood and Verrall [A Bayesian reliability growth model for computer software, Appl. Stat. 22 (1973) 332–346] and they modeled hazard rate as a random variable. In this paper, a new Bayesian software reliability model is proposed by combining two prior distributions for predicting the total number of failures and the next failure time of the software. The popular and realistic Jelinski and Moranda (J&M) model is taken as a base for bringing out this model by applying Bayesian approach. It is assumed that one of the parameters of JM model N, number of faults in the software follows uniform prior distribution and another failure rate parameter Φi follows gama prior distribution. The joint prior p(N, Φi) is obtained by combining the above two prior distributions. In this Bayesian model, the time between failures follow exponential distribution with failure rate parameter with stochastically decreasing order on successive failure time intervals. The reasoning for the assumption on the parameter is that the intention of the software tester to improve the software quality by the correction of each failure. With Bayesian approach, the predictive distribution has been arrived at by combining exponential Time between Failures (TBFs) and joint prior p(N, Φi). For the parameter estimation, maximum likelihood estimation (MLE) method has been adopted. The proposed Bayesian software reliability model has been applied to two sets of act. The proposed model has been applied to two sets of actual software failure data and it has been observed that the predicted failure times as per the proposed model are closer to the actual failure times. The predicted failure times based on Littlewood–Verall (LV) model is also computed. Sum of square errors (SSE) criteria has been used for comparing the actual time between failures and predicted time between failures based on proposed model and LV model.

Download Full-text