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CITS5502 Software Processes

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  • Course Code: CITS5502
  • University: University Of Western Australia
  • Country: Australia

Question:

Assignment tasks

  1. Averaging data

From the spreadsheet, you should randomly select the data of 7 out of the 14 students, and computer the mean time taken to write each version of each task,  giving you 12 means (3 tasks × 4 versions = 12). Your subsequent curve fifitting task and your report should be based on these 12 data points.

  1. Curve fifitting

You should choose any two of the following four models and use them to fifit curves to the

completion times for each task (i.e., for each of your chosen models, there are only 4 data

points available for curve fifitting, and you need to repeat this curve fifitting process for

tasks P1LA, P2LA, and P1LB).

The four models available are functions of time (t), and predict effffort (in person-minutes),

and have parameters a, b and c which need to be fifitted.

  • Model M1: Effffort = a + bt + ct 2
  • Model M2: Effffort = a + bct bt + 1
  • Model M3: Effffort = (a a c)e bt + c
  • Model M4: Effffort = (a a c)(t + 1) b + c

Here, t = 0, 1, 2 or 3, and denotes each of the four attempts to complete a task; “Effffort” is the person-time in minutes required to complete a task. You will need to impose the constraints that a > 0, b > 0, and c > 0, and you may need to apply a stronger constraint

on parameter c.

  1. Report

Write a report which uses the data and your fifitted models to discuss the following issues, and answer the questions raised.

  1. Brieflfly describe which two models you chose for further analysis and why. Give an interpretation of what each of the parameters a, b and c signify in the models.

Discuss what software you used for curve fifitting.

  1. Show charts of your data and the fifitted curves. Based on your curve fifitting results:
  • Describe which model fifits the best and which model is inappropriate (if applicable).
  • Discuss the extent to which learning from a previous project carries over to another project of a similar type, but in a difffferent language.

2• Discuss the extent to which in solving one project in a language improves performance in solving another project in the same language.

  • Discuss the degree to which practice should enable programmers to produce a more predictable estimate of effffort.
  • Is the learning pattern similar for the three difffferent tasks? Explain your answer.
  • What appears to be the minimum time that could be taken to solve any of the 3 problems?
  • Discuss any limitations of the data or the analysis, and what you would recommend in order to overcome these.
  • Provide a short conclusion section (half a page) summarizing your fifindings.
 

Answer:

Introduction:

In this task the main objective is to perform task 1 and task 2 to have an insight about the time needed to write problems in different languages when written repeatedly. In particular at first 7 random students’ required time to write problem 1 in language A and B and problem 2 in language A is selected from a total of 14 students’ data. Then the average time is calculated for each attempt for the three task and hence a total of 12 means are obtained. Then two chosen models with respect to time(t) is fitted with each 4 set of effort(person-minuites) data and the constants of the model is determined for which the model will approximate the actual data. Additionally, some graphs about the comparison of effort for different languages and different problems are drawn with respect to number of attempts to compare the languages or problems.

The time needed to do the task P1LA, P2LA and P1LB in four attempts at an interval 3 days by the 14 students are given below.

Time

0

1

2

3

0

1

2

3

0

1

2

3

 

Problem 1     Language A

Problem 2     Language A

Problem 1     Language B

1

175

140

211

73

208

154

144

128

137

111

113

101

2

370

110

30

25

286

53

36

27

100

44

32

13

3

270

170

117

114

50

59

45

40

105

80

60

60

4

80

35

16

13

78

42

39

19

113

50

29

29

5

80

23

30

13

59

33

20

13

139

63

28

22

6

55

20

19

18

83

15

13

13

115

35

29

41

7

127

46

50

56

97

46

67

39

115

50

56

52

8

265

70

63

21

103

42

44

31

146

97

92

58

9

205

165

58

64

150

115

107

102

159

113

87

82

10

52

18

71

21

34

17

18

16

17

15

21

14

11

210

71

63

26

60

31

21

18

38

32

71

24

12

169

82

47

27

73

38

91

58

106

22

28

28

13

212

253

54

47

46

34

25

17

73

32

26

19

14

150

75

63

45

75

78

27

26

73

135

28

66


In this task 7 out of 14 students are selected at random in excel by using the excel function (choose(randbetween(1,14),time 1, time 2,…time 14). Now, after generating the time needed of a randomly chosen 7 students, the values are pasted to stop them from updating. The generated times and the average is shown below.

The average times of 7 students in each attempt of solving three tasks are shown in bold at the bottom row.

Now, in this task two models are chosen to fit the effort from each attempt with respect to the time. Hence, each attempt has 4 data points which are fitted with the effort calculated from models.

The chosen models are

  • Model M2= Effort = (a+bct)/(bt+1)
  • Model M4 = Effort = (a-c)(t+1)^(-b) + c

Now, the error for fitting is estimated by calculating the absolute value of model effort and the obtained effort from the average time needed for 7 students (Wang et al. 2016).

The values of a,b and c are calculated by using the excel solver in which the objective function is the total error which is to minimized by varying the constants a,b and c subjected to constraints a, b and c are greater than zero (Winston 2016).

 

P1LA M2 effort table:

The a,b,c constants are

a

b

c

141.032646

0.944278

5.677957


The most appropriate model to represent the actual values of required time is model with minimum absolute error. Hence, from the above graphs it can be seen that the model M2 when fitted for the time data of solving problem 2 by language A produces the least absolute error of 40.46. Hence, model M2(effort = (a+bct)/(bt+1)) is the most appropriate model.

Hence, the minimum time that can be taken to solve any of the three problems is approximately 37 mins as this is lowest time required in the above tables.

Now, the effort in mins to write program 1 is compared for two different language A and B with respect to the attempts by the following graph.

Hence, it is clear the writing program 1 in language B is easier as the effort in mins is less in this case.

Again, the efforts(mins) w.r.t number of attempts is compared for writing program 1 and program 2 in language A and for different language with different problems (problem 1 and problem 2).

Hence, from the above two graphs it is clear that both of the model approximately predicts the same effort for writing problem 1 in different languages.

Conclusion:

Hence, the objectives of this experiment have been successfully met as the average time calculation from randomly selected times and curve fitting with the help of excel solver is performed with least errors and the visualizations through different graphs are obtained which clearly shows the comparison between the problems, languages and the trend of effort time with respect to attempts. As the minimum errors in some model are quite large when fitted with some of the Effort data, some other models can be selected from four models or a custom model can be created to minimize the absolute errors. The above graphs also show that when writing the same problem in consecutively in the same language the time needed to write the codes is reduced and this is true for writing in some other language also. Hence, this proves that when the algorithm to solve a particular problem is created then it becomes easier to write in any languages. Hence, creating the proper, structured algorithm for the problem is the essential part of programming.

 

References:

Wang, B., Zhou, S., Liu, W. and Mo, Y., 2015. Indoor Localization Based on Curve Fitting and Location Search Using Received Signal Strength. IEEE Trans. Industrial Electronics, 62(1), pp.572-582.

Winston, W., 2016. Microsoft Excel data analysis and business modeling. Microsoft press.

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