System and method for software estimation

• US 7,328,202 B2
• Filed: 08/18/2004
• Issued: 02/05/2008
• Est. Priority Date: 08/18/2004
• Status: Active Grant

• Current Assignee: Danny Siu Kau Ho, Jing Ren, Luiz F. Capretz, Xishi Huang
• Original Assignee: Danny Siu Kau Ho, Jing Ren, Luiz F. Capretz, Xishi Huang
• Inventors: Ren, Jing, Capretz, Luiz F., Huang, Xishi, Ho, Danny Siu Kau
• Primary Examiner(s): Vincent; David
• Assistant Examiner(s): Wong; Lut
• Application Number:
  US10/920,236
• Publication Number:
  US 20060041857A1
• Time in Patent Office: 1,266 Days
• Field of Search: 706/52, 706/2, 706/15
• US Class Current: 706/52
• CPC Class Codes: G06F 11/3616 using software metrics
• CPC Class Codes: G06N 3/043 based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]

Abstract

A system and method for software estimation. In one embodiment, the software estimation system comprises a pre-processing neuro-fuzzy inference system used to resolve the effect of dependencies among contributing factors to produce adjusted rating values for the contributing factors, a neuro-fuzzy bank used to calibrate the contributing factors by mapping the adjusted rating values for the contributing factors to generate corresponding numerical parameter values, and a module that applies an algorithmic model (e.g. COCOMO) to produce one or more software output metrics.

Claims

1. A computer-implemented software estimation system for use in software engineering, comprising a processor and a memory for storing components comprising:

a) at least one neuro-fuzzy component, wherein each neuro-fuzzy component has the learning capability of a neural network and implements a plurality of fuzzy rules, wherein said at least one neuro-fuzzy component takes as input a plurality of contributing factor ratings and processes said contributing factor ratings in accordance with said fuzzy rules to compute numerical parameter values for an algorithmic model, and wherein said at least one neuro-fuzzy component comprises;

i) a neuro-fuzzy inference system for resolving the effect of dependencies among a plurality of contributing factors associated with said plurality of contributing factor ratings, wherein said neuro-fuzzy inference system implements a first subset of said plurality of fuzzy rules, and wherein said neuro-fuzzy inference system takes as input said plurality of contributing factor ratings and processes each of said plurality of contributing factor ratings in accordance with said first subset to compute a plurality of adjusted ratings; and

ii) a neuro-fuzzy bank coupled to said neuro-fuzzy inference system, wherein said neuro-fuzzy bank implements a second subset of said plurality of fuzzy rules, and wherein said neuro-fuzzy bank takes as input said plurality of adjusted ratings and processes each of said plurality of adjusted ratings in accordance with said second subset to compute said numerical parameter values; and

b) an algorithmic model module coupled to said at least one neuro-fuzzy component, wherein said module takes as input said numerical parameter values and processes said numerical parameter values in accordance with said algorithmic model to compute at least one output metric;

wherein each output metric provides an estimate of a characteristic associated with a software development project, said at least one output metric for use in analyzing the feasibility of the software development project;

wherein said first subset of said plurality of fuzzy rules is defined by

Fuzzy Rule (i,k);

IF (RF₁ is A_1jik) AND (RF₂ is A_2jik) AND . . . AND (RF_N is A_Njik) THEN ARF_i=PFP_ik·

RF_i=1,2, . . . , N, k=1,2, . . . , M₁

where RF_i, is a rating of contributing factor i, ARF_i, is an adjusted rating of contributing factor i, M_i, is a number of fuzzy rules with contributing factor i as a consequent, PFP_ik is an adjustable parameter associated with the fuzzy rule (i,k), and A_sjik, is a fuzzy set associated with a i_ik-th rating level of contributing factor s for fuzzy rule (i,k);

wherein said plurality of adjusted ratings computed by said neuro-fuzzy inference system satisfy;

2. The software estimation system of claim 1, wherein said membership function of the fuzzy set A_sjik is a triangular membership function.

3. The software estimation system of claim 1, wherein said membership function of the fuzzy set A_ik is a triangular membership function.

4. The software estimation system of claim 1, wherein said numerical parameter values are subject to the following monotonic constraints:

FMP_i1≦

FMP_i2≦

. . . ≦

FMP_iNi , i ε

I_INC(F)

FMP_i1≧

FMP_i2≧

. . . ≧

FMP_iNi , i ε

I_DEC(F)

where I_INC(F) is the index set of increasing contributing factors whose higher rating value corresponds to the higher values of said at least one output metric, and I_DEC(F) is the index set of decreasing contributing factors whose higher rating value corresponds to the lower value of said at least one output metric.

5. The software estimation system of claim 1, wherein said algorithmic model is the constructive cost model COCOMO.

6. The software estimation system of claim 5, wherein said at least one output metric is an estimate of development effort associated with said software development project.

7. The software estimation system of claim 1, wherein each of said at least one output metric is an estimate of one of the elements selected from the following group:

software development cost, software maintenance cost, software quality, software development productivity, software size, scheduling, staffing, and defect prediction.

8. A software estimation method comprising the steps of:

a) computing numerical parameter values for an algorithmic model in at least one neuro-fuzzy component, wherein each neuro-fuzzy component has the learning capability of a neural network and implements a plurality of fuzzy rules, wherein said at least one neuro-fuzzy component takes as input a plurality of contributing factor ratings and processes said contributing factor ratings in accordance with said fuzzy rules, and wherein said at least one neuro-fuzzy component comprises;

i) a neuro-fuzzy inference system for resolving the effect of dependencies among a plurality of contributing factors associated with said plurality of contributing factor ratings, wherein said neuro-fuzzy inference system implements a first subset of said plurality of fuzzy rules, and wherein said neuro-fuzzy inference system takes as input said plurality of contributing factor ratings and processes each of said plurality of contributing factor ratings in accordance with said first subset to compute a plurality of adjusted ratings; and

ii) a neuro-fuzzy bank coupled to said neuro-fuzzy inference system, wherein said neuro-fuzzy bank implements a second subset of said plurality of fuzzy rules, and wherein said neuro-fuzzy bank takes as input said plurality of adjusted ratings and processes each of said plurality of adjusted ratings in accordance with said second subset to compute said numerical parameter values; and

b) computing at least one output metric in an algorithmic model module, wherein said module takes as input said numerical parameter values and processes said numerical parameter values in accordance with said algorithmic model, and wherein each output metric provides an estimate of a characteristic associated with a software development project, said at least one output metric for use in analyzing the feasibility of the software development projects;

wherein said first subset of said plurality of fuzzy rules is defined by

Fuzzy Rule (i,k);

IF (RF₁ is A_1jik) AND (RF₂ is A_2jik) AND . . . AND (RE_N is A_Njik) THEN ARF_i=PFP_k·

RF_i, i =1, 2, . . . N, k=1, 2, . . . , M_i

where RF_i is a rating of contributing factor i, ARF_i is an adjusted rating of contributing factor i, M_i is a number of fuzzy rules with contributing factor i as a consequent, PEP_ik is an adjustable parameter associated with the fuzzy rule (i,k), and A_sjik is a fuzzy set associated with a i_ik-th rating level of contributing factor s for fuzzy rule (i,k);

wherein said plurality of adjusted ratings computed by said neuro-fuzzy inference system satisfy;

9. The software estimation method of claim 8, wherein said membership function of the fuzzy set A_sjik is a triangular membership function.

10. The software estimation method of claim 8, wherein said membership function of the fuzzy set A_jk is a triangular membership function.

11. The software estimation method of claim 8, wherein said numerical parameter values are subject to the following monotonic constraints:

FMP_i1≦

FMP_i2≦

. . . ≦

FMP_iNi, i ∈

I_INC(F)

FMP_i1≧

FMP_i2≧

. . . ≧

FMP_iNi, i ∈

I_DEC(F)

where I_INC(F) is the index set of increasing contributing factors whose higher rating value corresponds to the higher values of said at least one output metric, and I_DEC(F) is the index set of decreasing contributing factors whose higher rating value corresponds to the lower value of said at least one output metric.

12. The software estimation method of claim 8, wherein said algorithmic model is the constructive cost model COCOMO.

13. The software estimation method of claim 12, wherein said at least one output metric is an estimate of development effort associated with said software development project.

14. The software estimation method of claim 8, wherein each of said at least one output metric is an estimate of one of the elements selected from the following group:

software development cost, software maintenance cost, software quality, software development productivity, software size, scheduling, staffing, and defect prediction.