CHAPTER 5 STAFFING LEVEL AND COST ANALYSES FOR SOFTWARE DEBUGGING ACTIVITIES THROUGH RATE- BASED SIMULATION APPROACHES

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1 101 CHAPTER 5 STAFFING LEVEL AND COST ANALYSES FOR SOFTWARE DEBUGGING ACTIVITIES THROUGH RATE- BASED SIMULATION APPROACHES 5.1 INTRODUCTION Many approaches have been given like rate based approaches for the reliability of the software and to analyze the reasons for the failure of the software. Criteria for the reliability of the software, number of debuggers or developers available are not taken into account. Newly detected faults have to wait for some time since all the debuggers will be busy in detecting the faults which they found previously. Time taken to remove the fault is taken into consideration and the main fact relies in it is that less number of faults been removed when compared to the number of faults detected. 5.2 IMPORTANCE OF DEBUGGING Debugging is the process of locating or fixing the error in software development coding or hardware. Debugging performance is mainly based on debugger s performance. So the debugger behavior should be analysed.

2 QUEUING SYSTEM Queuing system has important three characteristics. The characteristics are as follows: Arrival Process : The probability density distribution that determines the fault arrivals in the system. Service Process : The probability density distribution that determines the fault correction process. Number of Servers : Number of servers available in the correction process. 5.4 PROPOSED ARCHITECTURE This approach proposes a rate-based simulation method incorporated with the queuing theory for the debugging behavior during the development of the software. G/G/ and G/G/m models have been used in our proposed method. This method is used for the real software failure. This approach helps to predict the debuggers performance and the cost effectiveness. In the figure 5.1, the proposed simulated model analyzed the influence on the performance, and the cost, related to software debugging when the number of allocated debuggers changes. It is important information to guide the project manager in the estimation and adjustment of the staffing needs and cost benefit for debugging the systems.

3 Figure 5.1 Increasing Debugging activities through Queuing Theory 103

4 Simulation-Based Approaches Although productive research has used neural network approaches to predict the increment of the failure process, most training algorithms for neural network approaches suffer from the overfitting problem. That is, the fitting bias of the training set is very slight regarding known data, but the bias is unpredictably large when new data are presented to the network. Determination of the proper number of neurons is another common problem in the field of neural network research. Moreover, Tausworthe and Lyu (2004) argued that most SRGM only focus on the failure observation during the test phase, or the operational phase. They reported that the assumptions of most SRG lead to the over-simplification of the failure process. Thus, general simulation techniques have been developed to relax certain unreasonable assumptions. For ease of discussion, we let {N(t), t 0be the stochastic failure process that represents the number of failures observed in an execution interval (0, t). The failure behavior is modeled by a failure rate, N(t). That is, the behavior of the stochastic process purely depends on the rate function for each state of the software system. The state is represented by the number of occurrences of the event. Tausworthe and Lyu (2004) proposed a set of simulation procedures. The simulation algorithm can be applied to each individual activity during SDLC. Later, Gokhale and Lyu (2004) proposed a simulation technique to analyze structure-based software reliability. They believed that the time required by fault repair should be considered explicitly. They also extended the simulation to the reliability assessment on the application level. Recently, Gokhale et al (2004). Further considered the possibility of imperfect debugging in the

5 105 simulation approach Gokhale and Lyu (2005). However, the existing system published simulation techniques seldom consider the limitations of debugging resources, and this oversight may not be reasonable. In practice, the number of qualified debuggers will be controlled during SDLC Inoue and Yamada (2003). In the next section, System uses queuing theory to model software fault correction activities through simulation procedures Simulation Procedures with Queuing Management Mechanism The FDP, and FCP are related but distinct. If {H(t),t 0denotes the stochastic process representing the number of faults corrected during (0,t), then H(t) N(t). The output of the fault identification system is the input to the fault correction system. The amount of lag is tightly related to the number of personnel dedicated to the fault correction activities. The related activities of testing and debugging are depicted. In the past, some researchers Gokhale (2007) have attempted to predict software debugging behavior using a queueing model. Musa reported that there are three resources that govern the pace of debugging, and each resource represents an important part of the software debugging process. He used a type of queueing model in the FCP. The queueing constraints on each debugger will be modeled as a classical queueing system with random arrivals, and random service. Both the identification, and correction of faults are considered to be time-invariant Poisson processes Huang et al (2003) in calendar time. Procedures 1, and 2, were developed using a Java programming language, and are discussed in detail.

6 Queuing and Reliability Concepts A general description of the queuing system Kanoun and Laprie (1994) can be given as customers arriving for service, waiting for service if it is not immediate, and leaving the system after being served. As seen from Figure. 5.1, if a queue model is used to sketch the debugging system, the intensity function of N(t), i.e., (t), can be viewed as the arrival rate of the queueing system. To estimate and predict (t), apply SRGM to calculate the failure intensity. Further, the probabilities for the increments of the failure process N(t) can be assigned as N(t+ t) N(t) = 0 with probability 1 (t) t + o( t) 1 with probability (t) t + o( t) 2 with probability o( t) (5.1) where the function o( t) is defined o( t)/ t=0, Limit t 0 This function indicates that the probability of multiple failures during a time interval (t, t) is negligible for small values of t. On the other hand, the number of service channels can be considered as the number of allocated debuggers, and the service rate can be used to depict a debugger s efficiency in the debugging system. The negative exponential distribution is a common assumption for service time, and it has the probability function P(0 T s t s ) = G(t s ) = 1-e -µ t s, t s 0 (5.2)

7 107 Also, P(T s > t s ) = 1- G(t s ) = e -µ t s (5.3) Thus, the probability that a debugger finishes the fault correction in the time interval (t s,t s t s, is t), given that it has already been in progress for time P(t s T s t s t T s > t s ) = = µ t (5.4) Procedure 1 Queueing and software reliability theories are incorporated to simulate FDP, and FCP. To simplify the problem, the constraint on the number of available debuggers is first constrained. void Simulation_Procedure (double stop_time, double dt) { double current_time = 0; int working_server = 0, max_server = 0; struct fault_info correction[max_size]; int cumulative_arrival = 0, cumulative_departure = 0; while ( current_time < stop_time) { Figure 5.2 (Procedure1 Continued)

8 108 DETECTING: if( occur() ){ working_server++; if (working_server > max_server) max_server = working_server; correction[cumulative_arrival].arrival_time = current_time; correction[cumulative_arrival++].state = CORRECTING; CORRECTING: for(int i = 0; i < cumulative_arrival; i++) { if (correction[i].state == CORRECTING && leave(current_time - correction[i].arrival_time)) { working_server--; correction[i].departure_time= current_time' correction[i].state = CORRECTED; cumulative_departure++; current_time += dt; Figure 5.2 Procedure 1

9 Procedure 2 void Simulation_Procedure (double stop_time, double dt, int staffing_level) { double current_time = 0; int working_server = 0; struct fault_info correction[max_size], waiting_queue{max_size]; int num_correction = 0,cumulative_arrival = 0, cumulative_departure =0; int queue_head = 0, queue_tail = 0; while (current_time < stop_time) { Figure 5.3 (Continued)

10 110 ALLOCATION: while (working_server < staffing_level && queue_head!=queue_tail) { waiting_queue[queue_head].state = OUT_OF_QUEUE; current_time; waiting_queue[queue_head++].departure_time = correction[num_correction].state = CORRECTING; DETECTING: if(occur()){ cumulative_arrival++; if (working_server >= staffing_level){ waiting_queue[queue_tail].state = ENQUEUE; waiting_queue[queue_tail++].arrival_time = current_time; else { working_server++; Figure 5.3 (Continued)

11 111 if (working_server > max_server) max_server = working_server; correction[num_correction].arrival_time = current_time; correction[num_correction++].state = CORRECTING; CORRECTING: for (int i=0; i<num_correction; i++) { if (correction[i].state == CORRECTING && leave(current_time - correction[i].arrival_time)) { working_server--; correction[i].departure_time = current_time; correction[i].state = CORRECTED; cumulative_departure++; current_time += dt; Figure 5.3 Procedure 2

12 112 The following assumptions are made: 1) The software system is subject to failures at random times caused by the manifestation of the remaining faults in the system. 2) All faults are independent, and equally detectable. The probability that a failure will be experienced during (t, t+ t) is approximately, and the probability that two or more failures will occur during (t, t+ t) is negligible. 3) The correction of faults takes non-negligible time, i.e., explicit repair. The probability that a fault is corrected in time interval (t s, t s t) is µ t. Further, fault removals do not affect the ongoing activities of fault detection. 4) No new faults are introduced during the correction process. 5) Available, and qualified debuggers are always sufficient. The debugging system is modeled by a queue system (G/G/ ). Each time a failure occurs, there is no lag to allot a debugger to the detected fault. Based on these assumptions, Procedure 1 was developed, and is depicted in Figure Procedure 1 accepts two parameters as inputs: the total number of execution time units, defined as stop_time; and the consumed time of each run, denoted by dt. The length of each time unit should be consistent with the failure data collection form. Further, each time unit is divided into a large number of runs, and the length of each run should be short enough that multiple events in a run are rare. That is, the variations of failure

13 113 rate in (t,t+dt) should be insignificant. In addition to the two inputs, certain variables are also used in the simulation to represent the major components of the debugging system, and to gather useful statistics. The variable current_time represents a clock, which also indicates the cumulative execution time to the present. It increases with the completion of each run. As this variable s value is increased, the simulator updates the state of the debugging system to reflect the activities of each component. Construct a structure, fault_info, which is given as struct fault_info { arrival_time; // the time to occupy a resource departure_time; // the time to release a resource state; // the current status of the fault The array correction, each element of which contains a fault_info, is used to keep track of the status of each fault. Further, working_server denotes the current number of busy debuggers, while max_server logs the number of utilized debuggers at peak time. Finally, cumulative_arrival, and cumulative_departure are integers used to count the numbers of cumulative detected, faults and cumulative removed faults, respectively. There are several ways to derive the simulation. This framework adopts a random-number generator, which is common. Following mathematical probability distributions, the generator is programmed to

14 114 generate arrivals, departures, and so on. During simulation, actions taken in each run consist of two steps: detecting, and correcting Detecting Process Following this FDP Gokhale et al (2006). At the outset of each run, the function occur () will be invoked to determine whether the testers detect a fault in this run. From the procedure, the probability that a failure occurs during (t,t+dt) is approximately (t) dt. Hence, each time the occur function is called, a (0.0, 1.0)-uniform random number x will be generated, and compared with (t) dt. This means that the testers may detect a fault if x is greater than (t) dt. Once the occur function returns 1, working_server is increased, the value of max_server is updated, and the state of the detected fault will be recorded. Lines in Procedure 1 show the activities taking place as a result of each failure occurrence Correcting Process Departing from the detection step, it commences diagnosing the status of each detected fault by checking all elements of the array correction Hu et al (2007). If an open-remaining fault (a detected but uncorrected fault) is found, the function leave(t s ) determines whether this fault will be corrected in this run. Similar to the occur function, the success of fault removal relies upon the comparison between µ dt, and the random number y. If y > µ dt, then the dedicated debugger successfully corrects this fault in this run. The necessary actions taken due to this successful repair are given in Lines Otherwise, this fault cannot be corrected at this time, and will be re-examined in the next run. From Assumption 3), we know that the necessary correction

15 115 time is non-negligible. Hence, the return of leave (0) is given as 0, ensuring that the fault detected in the current run will not be removed immediately. The above two steps will be repeated until the end of the entire interval, when current_time = stop_time. After simulation, the total number of detected faults, the cumulative number of corrected faults, the maximum number of utilized debuggers, and the FDP are obtained. The FCP can also further be described. During software testing and debugging, project managers can collect all failure data prior to the arbitrary time t i, estimate the failure rate posterior to t i, exercise the simulation at time t i, and predict possible behaviors in the future, i.e., the behaviors in the time period t(t i ). The frequency of making a prediction (i.e.,how far ahead the prediction should be made) depends upon the policies of management. 5.5 RESULTS The first data set (DS1) Musa (1975) was from system T1 of the Rome Air Development Center project. The failure data were carefully collected under strict supervision. System T1 was applied to real time command and control, including 21,700 delivered object instructions. Over the course of 21 weeks, 9 programmers detected and removed 136 faults.

16 116 Table 5.1 Debugger s utilization Limitation of Available Debuggers Throughput (by the end of 21 weeks) Openremaining Avg. removals faults Time to remove faults (weeks) 2,3 Avg. waiting time (weeks) 3 Avg. response Avg. queue time lgth 3 (weeks) 3 Debugger utilization (%) Unlimited * * * * * * * The average of fault removals per week. 2 The faults detected during the period of 21 weeks. 3 The average measure of all faults. Note that the waiting time means the time of the fault pending in waiting queue, and the response time indicates the total time spent in the queuing system. Besides, because some detected faults may not be removed yet at the end of 21 weeks Huang et al (2006), the simulation of correction processes, and the statistics are continued until all pending faults are addressed.

17 117 Figure 5.4 Performance comparisons between different staffing levels as the value of varies from 1 to 8

18 Debuger's salary Penalties of Open-Remaining faults Number of Debuggers Figure 5.5 Relationship between debuggers and expected cost According to the above analyses, the limitation on the debugging team size is not the bottleneck to enhance performance when the number of personnel is more than 7. Increasing the number of debuggers can only increase the number of simultaneous working debuggers, but it does not reduce the consumed time taken for a debugger to fix a fault. Therefore, when the staffing level can bear the load, more debuggers cannot improve the throughput. If the manager wants to ameliorate the performance, it is necessary to improve the debuggers skills, i.e. to increase the value of Figure 5.4 shows some performance comparisons between different staffing levels as the value of varies from 1 to 8.The utilization of 6 debuggers is very low at the beginning. However, because the number of detected faults grows rapidly from the 8th week to the 18th week, 6 personnel seem unable to bear the load. Re-staffing may be reasonable in both conditions. Using the pro- posed framework, project managers can easily estimate the influence

19 119 caused by re-staffing the debugging system, and further decide whether to make an adjustment. In practice, the resources of a project are often estimated by expert judgment. That is, several experts on software development techniques estimate the needs individually, consult with each other, and reach an agreed estimate. When the experts discuss the allocation of staff to specific Work Breakdown Structure tasks, they may also benefit from the outputs of the proposed approaches. Moreover, the information on completed projects (the source cases) is valuable as input for preliminary planning of a similar new project (the target case). This common technique is called estimation by analogy (also called case-based reasoning). For the completed projects, we can also use the developed framework to retrace and reconstruct the possible debugging processes under different amounts of debugging personnel. Such analyses provide guidance for estimating the staffing needs of new projects with similar characteristics. 5.6 CONCLUSION The proposed framework helps to understand about the on-going project and also used to infer the current and future situations in the project or reconstruct the project. The applications of the proposed procedures are explained through two real data sets. This studies show that the proposed simulation procedures can analyze the influence on the performance, and the cost related to software debugging when the number of allocated debuggers changes. This useful, important information can guide project managers in the estimation and adjustment of the staffing needs for debugging systems.

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