The aim of this research is the development of the information technology (model, method and tools) of predicting the characteristics and evaluating the success of the software projects implementation based on the analysis of the software requirements specification (ITPCES). ITPCES structure was first time proposed. One of the non-realized components of ITPCES is the intelligent system of predicting the characteristics and evaluating the success of the software projects implementation (SPCES). This research is devoted to design of SPCES and experiments with it. SPCES gives the conclusion about probably category of success of the software project implementation by analyzing the software requirements specification (at the early stages of the life cycle). The SPCES conclusions provide to the customer the ability of the comparing the proposed software projects and provide to the customer the data for the grounded and informed choice of the most successful software project.
In recent years, the software industry has reached a level of evolution where the
development of software systems is user-oriented [
- 88
of software projects are challenged and 18% of software projects are failed, i.e. 61%
software projects aren't successful and qualitative - Fig. 1.
In [
Then the actual task is the ability to evaluate the potential success of software project implementation based on the software project characteristics (project cost, duration, complexity, usability, cross-platform, quality), the predicted values of which can be obtained by analyzing the SRS indicators. The success of software project implementation is timely execution of software project within the allocated budget and with realization of all necessary features and functionality.
The analysis of SRS structure [
The analysis of the methods and tools for determination of the software projects
characteristics [
The task of this research is the development of the information technology (model, method and tools) of predicting the characteristics and evaluating the success of software projects implementation based on analysis of the SRS. 2
Considering the definition of information technology [
Fig. 2 shows that the basis of ITPCES is the developed neuronet model of predicting the software projects characteristics based on the SRS analysis and method of evaluating the success of software projects implementation based on analysis of SRS (MESSPI) and also intelligent system of predicting the characteristics and evaluating the success of software projects implementation, which should be design.
The neuronet model of predicting the software projects characteristics based on
the SRS analysis was developed for evaluation the software projects characteristics
based on the processing of SRS indicators [
The structure chart of the ANN layers in Simulink is shown on Fig. 3.
The developed method of evaluating the success of software project
implementation based on analysis of SRS (MESSPI) consists of the next stages [
IipSp=0.5*0.866*(Cs*Cx+Cx*Dsp+ Dsp*Ub+ Ub*Cp+Cp*Qs+ Qs*Cs) (1) 3. evaluation of the degree of success of the software project implementation on the basis of the integrative indicator:
PIip=IipSp/Iipmax=IipSp/2.598=0.385*IipSp (2) 4. testing of the stability and acceptability of compensations of software project characteristics: the indicator AceSp of stability and acceptability of compensatory effects of the characteristics has the value “True”, if the hexagon (Fig. 4) is convex (if the sum of the angles of hexagon is 720° and sines of angles have the same signs).
Thus, the input data for MESSPI is the set of SRS indicators, and the result of the
method is the evaluation of the degree of success of the software project
implementation [
For the completion of ITPCES we need to develop the intelligent system of
predicting the characteristics and evaluating the success of software projects
implementation based on the developed method MESSPI [
The input of the intelligent system of predicting the characteristics and evaluating the
success of software projects implementation (SPCES) are the selected in [
The structure (algorithmic-focused vision with elements of architectural solutions) of the intelligent system of predicting the characteristics and evaluating the success of software projects implementation is represented on Fig. 5.
SPCES consists of the next components:
1. module of introduction of the SRS analysis – is the part of the user interface; reads
the user information about the quantitative values of 24 SRS indicators, which are
necessary for prediction of the software project characteristics;
2. module of the user support – is the part of the user interface; provides to the user
the information about the structure of the software requirements specification,
about the SRS indicators (which are required for prediction of the software projects
characteristics), about the valid (for the system) ranges of the SRS indicators
values (defined in [
The rules for the testing of acceptability of input values of SRS indicators (are
substantiated by the valid (for the SPCES) ranges of the SRS indicators values, that were
defined in [
The rules for the forming of ANN input vectors (are substantiated by the quantities of the elements of the above-described sets R1-R3) have the form: 1. on the input xʹi (i=1..5) the value of i-th element of set R1 of the indicators of the section 1 of the SRS is submitted; 2. on the input xʺk (k=1..6) the value of k-th element of set R2 of the indicators of the section 2 of the SRS is submitted; 3. on the input xj (j=1..13) the value of j-th element of set R3 of the indicators of the section 3 of the SRS is submitted; 4. if the user doesn’t enter the value of indicator, then corresponding input of ANN is -1.
The rules for the testing and preparation of ANN results to the display (are substantiated by the above-described approach to ANN training) have the form: 1. if Cs=0 or Dsp=0 or Cx=0 or Cp=0 or Ub=0 or Qs=0, then insufficient data for prediction of the software project characteristics, in this case the method MESSPI and the system SPCES cannot be used for this SRS and project; 2. output y1 - Cs – the relative value of the software project cost, output y2 – Dsp – the relative value of the software project duration, output y3 - Cx – the relative value of the software project complexity, output y4 – Ub – the relative value of the software project usability, output y5 – Cp – the relative value of the software project cross-platform, output y6 – Qs – the relative value of the software project quality.
The rules for the testing of the stability and acceptability of compensatory effects of the software project characteristics (are substantiated by the above-described method MESSPI) have the form: 1. if the hexagon (Fig. 4) is convex (if the sum of the angles of hexagon is 720° and sines of angles have the same signs), then the indicator AceSp of stability and acceptability of compensatory effects of the characteristics has the value “True”; 2. if the hexagon (Fig. 4) isn’t convex (if the sum of the angles of hexagon isn’t 720° or sines of angles have the different signs), then the indicator AceSp of stability and acceptability of compensatory effects of the characteristics has the value “False”; 3. if AceSp=True, then the chararcteristics are stable, the compensations of characteristics are acceptable, the method MESSPI and the system SPCES are suitable for this software project and this SRS, else if AceSp=False, then the chararcteristics are unstable, the compensations of characteristics are unacceptable, the method MESSPI and the system SPCES are not suitable for this software project and this SRS.
The degree of success of the software project implementation, which is defined
under the 3-rd stage of method MESSPI, is uninformative to the developers and to the
customers through the complexity and ambiguity of interpretation of its value in the
predicting the category of the success of the software project. For the facilitation of
the interpretation of the value of the degree of success of the software project
implementation we define thresholds values of this degree, which provide the
conclusion about the category of the project success. For establishment of these
thresholds values (for creation of the rules for the forming of the conclusion about
category of success of the software project implementation) we have analyzed 410
above-described SRS, for which the degrees PIip of success of the software project
implementation were determined according to method MESSPI [
The rules for the forming of the conclusion about category of success of the
software project implementation (considering the empirical estimates from Table 1,
which in general correspond to the statistical evaluations [
PIip≤0.19, then software project is predictably failed; 2. if the value of the degree of success of the software project implementation 0.19<PIip≤0.62, then software project is predictably challenged; 3. if the value of the degree of success of the software project implementation
PIip>0.62, then software project is predictably successful. 4
The input data for the SPCES are the SRS indicators for five software projects, that were developed by the different groups of developers for the solution of the one task – the development of the automated system for large-format photo print – to the order by LLC «Deymos», Khmelnitsky (Table 2).
After the introduction of the SRS indicators values the module of the previous processing of the input SRS indicators saves this data in the data section of the knowledge base and tests the acceptability of the input values of the SRS indicators under the rules of the rules section of the knowledge base - if input values are not valid, then the system gives the message to the user: "The input values of SRS indicators are unacceptable, the system of predicting the characteristics and evaluating the success of software projects implementation cannot be used for such SRS". For the proposed software projects the valid values of the SRS indicators were entered, so the ANN input vector is formed for each software project. Block of the forming of ANN input vectors generates the vectors for the appropriate ANN inputs - Table 3. The ANN of predicting the software project characteristics of the project processes the input vector and gives the results (the predicted relative evaluations of the software project characteristics), that also is shown in Table 3.
Block of the testing and preparation of ANN results to the display tests the ANN results – if the value of even one ANN output is 0, the system gives the message to the user: "The data for predicting the software project characteristics are insufficient, so the system of predicting the characteristics and evaluating the success of software projects implementation cannot be used for such SRS". For the proposed software projects the input data were sufficient, so the ANN results were prepared to the display according to the above rules.
Block of the testing of the stability and acceptability of compensatory effects of the software project characteristics calculates the indicator AceSp of stability and acceptability of compensatory effects of the characteristics (Table 4) and forms the conclusion about stability and acceptability of compensatory effects of the software project characteristics. If AceSp = False, the user gets the message: "The software projects characteristics are unstable, the compensations of characteristics are unacceptable, so the system of predicting the characteristics and evaluating the success of software projects implementation is not suitable for this project and for this SRS". If AceSp = True, then the system calculates the integrative indicator of software project, the degree of success of the software project implementation and forms the conclusion about category of software project implementation success. For the proposed software projects №1, №2, №3, №5 the characteristics are stable, the compensations of characteristics are acceptable, so the obtained predicted relative values of the characteristics are processed according to the method MESSPI. For the proposed software project №4 the system gives the message to the user: "The software projects characteristics are unstable, the compensations of characteristics are unacceptable, so the system of predicting the characteristics and evaluating the success of software projects implementation is not suitable for this project and for this SRS".
Block of the forming of the integrative indicator of software project forms the graphical representation and calculates the value of integrative indicator IipSp of software project (Table 4).
Block of the evaluation of the degree of success of the software project implementation estimates the value of the degree of success PIip of the software project implementation (Table 4).
Block of the forming of the conclusion about category of software project implementation success uses the rules of the knowledge base and forms the conclusion about category of software project implementation success (Table 4).
Let's analyze of the results: the category of software project implementation success was defined for the projects №1, №2, №3, №5, for which the predicted values of characteristics are stable, their compensations are acceptable. The software project №1 has the best characteristics, it predictably belongs to the category of successful projects. The software projects №2, №3, №5 have the worst characteristics and predictable are classified as challenged projects. For the project №4 the system SPCES cannot determine the category of implementation success because the predicted characteristics are unstable and their compensations are unacceptable. So the conclusion of the intelligent system of predicting the characteristics and evaluating the success of software projects implementation recommends to LLC "Deymos" to order the implementation of software project №1 (to the development of the automated system for large-format photo print) that will be successful with the greatest probability.
Nowadays the developer and customer select the software project based on only own intuition and the cost and duration that predicted in the SRS. But SRS developers cannot always correctly predict the oriented cost and duration of software project during development of the SRS. Predicted (in the SRS) values of cost and duration for the four examined alternative software projects (for which SPCES determines the category of implementation success) are represented in Table 5.
The values of the characteristics of software projects from Table 5 show, that all four software projects have the different duration but the same cost that predicted in the SRS. But the results of Table 3 show, that projects have significantly different relative values of all characteristics, including the cost, which were calculated taking into account all significant SRS indicators. Thus, the relative cost ranges from 0.518 (for Project №2) to 0.789 (for Project №1). So, if we evaluate the cost, taking into account all significant SRS indicators, then it values are not the same for the four examined projects. As for the value of project duration, this value isn't defined for project №5, for example, then software project in this case will be evaluated solely on the basis of its cost value. Therefore, the customer and developer can make the wrong conclusion about choice of project on the basis of solely cost and time that predicted in the SRS. In addition, such conclusion is difficult in the real conditions. For example, according to Table 5, the lowest cost has the software project №3, and the lowest duration has the software project №1 (but the value is unknown for the software project №5), i.e. the customer and the developer must make the choice of software project in this case on the basis of one criterion - or by cost, or by the duration.
In addition, the success of software projects implementation depends not only on the cost and duration, but also on the functionalities of developed software, i.e. on the rest of the main characteristics of the software project - complexity, usability, crossplatform and quality, which aren't defined in the SRS explicitly in the quantitative form. In addition, Table 4 shows that the examined software projects have the different category of software project implementation success. Therefore, the values of main characteristics, provided by ANN and the conclusions of SPCES about the category of software project implementation success will help to make the right choice and to implement the software project which will be successful with the greatest probability (among from four examined software projects is Project №1). But if the developer and the customer made the choice of the software project on the basis of the only duration, they probably would choose the project №3, which really has a low degree of success of the implementation and with the high probability will be challenged software project (wrong choice). 5
In the article the structure of information technology of predicting the characteristics and evaluating the success of software projects implementation (ITPCES) is first time proposed. The basic components of ITPCES are the previously developed by the author the neuronet model of predicting the software projects characteristics and the method of evaluating the success of software projects implementation based on analysis of SRS and also (yet not developed) the intelligent system of predicting the characteristics and evaluating the success of software projects implementation, to the designing of which this research is dedicated.
The structure of the intelligent system of predicting the characteristics and evaluating the success of software projects implementation (SPCES) are proposed. SPCES consists of the next components: module of introduction of the SRS analysis; module of the user support; module of the previous processing of the input SRS indicators; knowledge base; artificial neural network (ANN); module of the analysis of ANN results; module of the results display. This system gives the conclusion about the probably category of success of the software project implementation based on analysis of the SRS (at the early stages of the life cycle).
The practical significance of the proposed information technology ITPCES is this fact, that system's conclusions about the category of the success of software project implementation provide to the customers the comparison of the proposed software projects and the data for the reasoned and informed choice of the most successful software project (not just on the basis of the project cost and duration, as is currently).
The authors’ following perspective for future researches are: 1) development of DEF0-block diagram and UML component/deployment diagrams for the SPCES; 2) realization of the intelligent system of predicting the characteristics and evaluating the success of software projects implementation for prediction of characteristics and evaluation of success of software project implementation based on analysis of the SRS; 3) realization of the information technology of predicting the characteristics and evaluating the success of software projects implementation.