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Software Engineering | Quasi renewal processes

  • Difficulty Level : Hard
  • Last Updated : 09 Oct, 2018
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Let {N(t), t > 0} be a counting process and let $X_n$ be the time between the $(n-1)_{th}$ and the $n_{th}$ event of this process,  n\geq 1.

Definition:
If the sequence of non-negative random variables {X1, X2, ….} is independent and

    $$X_i=aX_{i-1}$$

for $i\geq 2$ where $\alpha > 0$ is a constant, then the counting process {N(t), t $\geq $ 0} is said to be a quasi-renewal process with parameter and the first inter-arrival time $X_1$.

When $\alpha $ = 1, this process becomes the ordinary renewal process. This quasi-renewal process can be used to model reliability growth processes in software testing phases and hardware burn-in stages for $\alpha $ > 1, and in hardware maintenance processes when $\alpha \geq $ 1.



Important formulae related to quasi-renewal processes:
Assume that the probability density function, cumulative distribution function, survival function and failure rate of random variable$ X_1\: are\: f_1(x), F_1(x), s_1(x), and\: r_1(x)$, respectively. Then,

  1. The pdf(probability density function) of Xn for n = 1, 2, 3, … is

        $$f_n(x)= \frac{1}{\alpha^{n-1}}f_1\left (\frac{1}{\alpha^{n-1}}x\right ) $$

  2. The cdf(cumulative density function) of Xn for n = 1, 2, 3, … is

        $$F_n(x)= F_1\left (\frac{1}{\alpha^{n-1}}x\right ) $$

  3. The survival function of Xn for n = 1, 2, 3, … is

        $$S_n(x)= S_1\left (\frac{1}{\alpha^{n-1}}x\right ) $$

  4. The failure rate of Xn for n = 1, 2, 3, … is

        $$ f_n(x)= \frac{1}{\alpha^{n-1}}r_1\left (\frac{1}{\alpha^{n-1}}x\right )$$

Similarly, the mean and variance of Xn is given as

    $$E(X_n)=\alpha^{n-1}E(X_1)$$ $$ Var(X_n)=\alpha^{2n-2}Var(X_1)$$

Because of the non-negativity of $X_1$ and the fact that $X_1$ is not identically 0, we obtain



    $$ E(X_1)=\mu \neq 0 $$

Proposition-1:
The shape parameters of $X_n$ are the same for n = 1, 2, 3, … for a quasi-renewal process if $X_1$ follows the gamma, Weibull, or log normal distribution. This means that after “renewal”, the shape parameters of the inter-arrival time will not change. In software reliability, the assumption that the software debugging process does not change the error-free distribution type seems reasonable.

Thus, the error-free times of software during the debugging phase modeled by a quasi-renewal process will have the same shape parameters. In this sense, a quasi-renewal process is suitable to model the software reliability growth. It is worthwhile to note that,

     $$ \lim_{n \rightarrow  \infty } \frac{E(X_1+X_2+ ... +X_n)}{n}&= \lim_{n \rightarrow  \infty }\frac{\mu_{1}(1-\alpha^n)}{(1-\alpha)n} $$ $$=\; 0 if\; \alpha\:  \: 1 $$

Therefore, if the inter-arrival time represents the error-free time of a software system, then the average error-free time approaches infinity when its debugging process is occurring for a long debugging time.

Proposition-2:
The first inter-arrival distribution of a quasi-renewal process uniquely determines its renewal function. If the inter-arrival time represents the error-free time (time to first failure), a quasi-renewal process can be used to model reliability growth for both software and hardware.

Suppose that all faults of software have the same chance of being detected. If the inter-arrival time of a quasi-renewal process represents the error-free time of a software system, then the expected number of software faults in the time interval [0, t] can be defined by the renewal function, m(t), with parameter \alpha > 1. Denoted by $m_r(t)$, the number of remaining software faults at time t, it follows that,

    $$m_r(t)=m(T_c)-m(t)$$

where $m(T_c)$ is the number of faults that will eventually be detected through a software lifecycle Tc.

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