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An automated software reliability prediction system for safety critical software

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Abstract

Software reliability is one of the most important software quality indicators. It is concerned with the probability that the software can execute without any unintended behavior in a given environment. In previous research we developed the Reliability Prediction System (RePS) methodology to predict the reliability of safety critical software such as those used in the nuclear industry. A RePS methodology relates the software engineering measures to software reliability using various models, and it was found that RePS’s using Extended Finite State Machine (EFSM) models and fault data collected through various software engineering measures possess the most satisfying prediction capability. In this research the EFSM-based RePS methodology is improved and implemented into a tool called Automated Reliability Prediction System (ARPS). The features of the ARPS tool are introduced with a simple case study. An experiment using human subjects was also conducted to evaluate the usability of the tool, and the results demonstrate that the ARPS tool can indeed help the analyst apply the EFSM-based RePS methodology with less number of errors and lower error criticality.

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Acknowledgments

This paper was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for any third party’s use, or the results of such use, of any information, apparatus, product, or process disclosed in this report, or represents that its use by such third party would not infringe privately owned rights. The views expressed in this paper are not necessarily those of the U.S. Nuclear Regulatory Commission. We are grateful to Kevin Smearsoll and Boyuan Li for supporting this research.

Author information

Authors and Affiliations

  1. Nuclear Engineering Program at The Ohio State University, Columbus, OH, USA

    Xiang Li

  2. Oblon, McClelland, Maier & Neustadt, LLP, Washingston D.C Metro Area, USA

    Chetan Mutha

  3. Department of Mechanical and Aerospace Engineering at The Ohio State University, Columbus, OH, USA

    Carol S. Smidts

Authors
  1. Xiang Li
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  2. Chetan Mutha
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  3. Carol S. Smidts
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Corresponding author

Correspondence to Xiang Li.

Additional information

Communicated by: Nachiappan Nagappan

Appendix

Appendix

Appendix. List of Acronyms Used in this Paper

A:

Solve a test problem using ARPS tool

ARPS:

Automated Reliability Prediction System

BBN:

Bayesian Belief Networks

EFSM:

Extended Finite State Machine

Ei :

The Execution probability of the i-th defect

EI :

Error index

F:

False

H:

Null hypothesis

HA :

Alternative hypothesis

HLEFSM:

High Level Extended Finite State Machine

IAP:

Incorrect/Ambiguous Predicate

Ii :

The Infection probability of the i-th defect

INP:

Information Not Post-processed

IP:

Information Post-processed

IV:

Internal Variables

LLEFSM:

Low Level Extended Finite State Machine

M:

Manually solve a test problem

NE :

Number of errors

OP:

Operational Profile

P:

Set of Predicates; Pressure

PC :

The correct predicate

pdf:

Probability density function

Pi :

The Propagation probability of the i-th defect

PIE:

Propagation, Infection and Execution analysis

pos:

Position

PO :

The original predicate

Pres:

Pressure

prev:

Previous

Prob:

Probability

Re:

Reliability

RePS:

Reliability Prediction System

S:

Set of States

Sat :

Satisfactory

SDD:

Software Design Document

SI:

State Initialized

SNI:

State Not Initialized

SRGM:

Software Reliability Growth Model

SRS:

Software Requirement Specification

SUS’s:

Software under study’s

T:

Set of Transactions; Temperature; True

T :

Time

T1:

Test #1

T2:

Test #2

Temp:

Temperature

V1_C:

Valve #1 closed

V1_O:

Valve #1 opened

V2_C:

Valve #2 closed

V2_O:

Valve #2 opened

V3_C:

Valve #3 closed

V3_O:

Valve #3 opened

VCS:

Valve Control System

Γ:

Output Variables

Σ:

Input Variables

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Li, X., Mutha, C. & Smidts, C.S. An automated software reliability prediction system for safety critical software. Empir Software Eng 21, 2413–2455 (2016). https://doi.org/10.1007/s10664-015-9412-6

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  • DOI: https://doi.org/10.1007/s10664-015-9412-6

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