The Simulators of Software Development Projects based on dynamic models have supposed a significant
advance in front of the traditional techniques of estimate. These simulators enable to know the evolution of
a project before, during and after the execution of the same one. But its use in the estimate of the project
before beginning the execution, has been braked by the great number of attributes of the project that it is
necessary to know previously. In this paper are presented the improvements that have been added to the
simulator developed in our department to facilitate the use of them, and a new improvement obtained when
using machine learning and fuzzy logic techniques with the databases generated by the simulator. In this last
case, the project manager can know, in function of the decisions that he takes, the level of execution of the
project objectives.Comisión Interministerial de Ciencia y Tecnología (CICYT) TIC99_035

Aroba Páez, Javier

Ramos Román, Isabel

Riquelme Santos, José Cristóbal

English

idUS. Depósito de Investigación Universidad de Sevilla

1IMPROVEMENTS IN THE DECISION MAKING INSOFTWARE PROJECTS I. Ramos Román , J. Riquelme SantosDpto. Lenguajes y Sistemas Informáticos. University of Sevill a.Avda. Reina Mercedes s/n. Sevill a, Spain. E-Mail : isabel.ramos@lsi.us.es, riquelme@lsi.us.esJ. Aroba PáezDpto. Ingeniería Electrónica, Sistemas Informáticos y Automática. University of Huelva.Ctra. Huelva-La Rabida s/n. Huelva , Spain. E-Mail : aroba@diesia.uhu.esKeywords: Simulation, projects estimation, fuzzy logic, machine learning.Abstract :       The Simulators of Software Development Projects based on dynamic models have supposed a significantadvance in front of the traditional techniques of estimate. These simulators enable to know the evolution ofa project before, during and after the execution of the same one. But its use in the estimate of the projectbefore beginning the execution, has been braked by the great number of attributes of the project that it isnecessary to know previously. In this paper are presented the improvements that have been added to thesimulator developed in our department to facilit ate the use of them, and a new improvement obtained whenusing machine learning and fuzzy logic techniques with the databases generated by the simulator. In this lastcase, the project manager can know, in function of the decisions that he takes, the level of execution of theproject objectives.1. INTRODUCTIONIn last years, the appearance of the dynamic modelsfor Software Projects (SP) and of potent simulationenvironments (Stella, Vensim, iThink, Powersim,etc.), has facilit ated the appearance of thedenominated Software Projects Simulator (SPS),that provides the capabilit y to simulate the behaviourof such projects. These simulators facilit ates to themanagers of projects to experiment with differentmanagement policies with a null cost, so that thedecisions taken are as correct as possible.Therefore, a SPS allows to carry out a:1. A priori Analysis of the project: Simulation ofthe project, before beginning the developmentprocess, under different management policiesand/or different initial estimates of resourcesand under the use of different developmenttechnologies.2. Post-mortem analysis of the project: Simulationof projects that have concluded already, in thoseit’s interesting to know how the obtained resultscould have been improved.3. Project monitoring: Simulation of the projectduring the development process, to adapt theproject estimations to the real evolution of thesame one.The SPS allows us to respond to questions of thetype: “What will it happen if?” before beginning,“What is it happening?” during the execution and“What would happened if?” once it has concluded(Figure 1).2The intuitive and natural idea contained in a dynamicmodel for SP, is that the evolution of the project andtherefore the attainment of the goals, depends of: theinitial estimate of the necessary resources to carryout the project, the management policies that areapplied and certain aspects related with the owndevelopment organisation (level of maturity,experience, environmental aspects, etc).The appearance, therefore, of the SPS it has meant asignificant advance in the management and estimateof projects. Even this way, these simulators presentan important inconvenience that have braked theiruse (to company level): the numbers of parameters(of the project and the development organisation)that should be known previously.One of our first works was centered in thedevelopment of a SPS (Ramos 1998) that allows tosimulate the behaviour of a project using half ofattributes that the model that appears in (Abdel1991). But we found an additional problem, commonto the process that bears the decisions making in anyproject: the diversity and simultaneity ofmanagement policies, and factors that influence onthe development process. That is, which is theappropriate combination of the attributes, to obtainsome concrete objectives. The machine learningtechniques allowed us to correct the previousproblem (Ramos 1999). These techniques, applied tothe databases that the SPS generates, provide to theproject manager information about the managementpolicies that he has to apply to get the objectives thatpreviously, has established. In this paper, we presenta step more in the way of improving the use of theSPS: the application of machine learning and fuzzylogic techniques.In the following sections are described firstly thedifferent contributions that have facilit ated the use,not only academic but also managerial, of the SPS.Next, is presented the information that we canextract when conjugating the use of a SPS withmachine learning and fuzzy logic techniques.2.     SOFTWARE PROJECTSIMULATORSThe basic variables that allow to know the behaviourof a dynamic system, and therefore the process ofsoftware development that we model by means ofthis system, are defined by means of differentialequations. The system also includes a series ofattributes that allow us to study the behaviour of thesame one. These behaviors come given by themanagement policies that can be applied in the SP,so much those related with the environment of theproject (number of tasks, time, cost, number oftechnicians, complexity of the product, etc.) as thoserelated with the development organisation.2.1   Initial situationTo simulate the behaviour of a SP by means of aSPS, the project manager would follow the followingsteps (Figure 2):1. Definition of the concrete values that will t akethe attributes (parameters and functions) of theproject.2. Simulation of the model by means of the SPS toobtain the evolution of those variables that themanager wish to analyze3. Analysis of the obtained results for thesevariables and check if this results coincide withthe goals of the project (cost, quality, time, etc).4. If the obtained results: Coincide with the goals of the project, theparameters will be known, and therefore themanagement policies that will enable us toobtain the wanted results in the project. Don't coincide with the goals of the project,it must be redefined again (step 1) and themanager must repeat the previous steps.Therefore, the project will be simulated somany times like be necessary untilobtaining the wanted results.BeforeDuringAfterInitial estimatesManagement PoliciesLevel of maturityFigure 1:  Possibiliti es of software projects                simulators3 Therefore, to diminish the number of times that theproject will be simulated, the project manager musthave an exhaustive level of knowledge of theconcrete values that will t ake the model parameters;This can be quite diff icult for two reasons: first,because the number of parameters that contains adynamic model and the multiple combinations thatcan exist between them, and second, becausefrequently, the project manager does not know theconcrete value that will t ake some parameters,although, he will have a clear idea of the interval ofvalues where they will move. For example, themanager doesn't possibly know if the average dailymanpower per staff in the project will be of 50% orof 24%, but he will know that it will be between a20% and a 60%.In the four steps described previously, only the step2 is carried out in an automatic way. In the rest of thesteps the project manager must intervene.2.2   Improvements to the initialsituationThe application of machine learning techniques,concretely the tool C4.5 (Quinlan 1993), allowed usto solve the previous problem. These techniquesenable us to obtain management or decision rules1.The knowledge of these management rules helps inthe decisions making to estimate the results wantedby the project manager. For the obtaining ofmanagement rules, we have conjugated theadvantages that provides the systems that learn basedon rules and the information that a dynamic modelprovides for SP (cost, delivery time, quality, etc).                                                       1 We call management rule to a set of managementpolicies (decisions) that the manager takes for carrying outthe project goals.Also, the obtaining of management rules allows tothe project director to analyze which are the mostsignificant management policies, to get theobjectives of the SP.In the Figure 3 is shown the process used for thesimulation of a SP and for the obtaining of thesemanagement rules, that allow to estimate these goals:1. Definition of: The intervals of values that can take theattributes of the project. The project goals that the managerwants to obtain. For example, it iswanted that the final cost of the projectdoesn't overcome to the initial in morethan 20% and that the final time ofdelivery doesn't overcome to the initialin more than 10%.2. Simulation of the project by means of the SPS.For simulating the model, the parameters takevalues randomly chosen inside the interval ofvalues defined previously.3. Automatic generation of a database. In eachsimulation a record of the database is obtained;in these records are stored the values obtainedfor the parameters and the final values of thevariables (obtained in the simulation) thatinterests us to analyse (cost, time, quality, etc.).Each register of the database represents apossible scenario of the project.4. Application of machine learning techniques.Taking the generated database as input, the toolC4.5 learns examining the sample of resolvedcases, and proposing a group of rules for thedecision making. The C4.5 is a hierchicalclassifier, that provides semi-qualitativeinformation.5. Obtaining of management rules.Figure 2 :  Steps in the simulation of a Software                  Project using a SPSSoftware ProjectSimulationResultsAnalysisDefinition of  theparameters valuesNoEndYesResults=GoalsFigure 3: Steps in the simulation of a project using a SPS                and machine learning techniques (Tool C4.5)Project Simulation  D. B. MachineLearningTecniquesManagementRules• Definition of goals• Definition of the inter-vals of values of  theparameters4In this case, the project manager should only carryout the first step, where the definition of theattributes is also facilit ated regarding the step 1 ofthe previous section (figure 2); the rest of thedescribed steps is carried out automaticallyAn example of management rule, obtained from thehierarchical tree that provides the C4.5, can be :"If the average daily manpower per staff is greaterthan 50% and the medium time of the technicians'incorporation in the project it is smaller than 5 daysand the dedication to formation it is then greaterthan 5% the wanted goals will be completed”In summary, the use of a tool for the obtaining ofmanagement rules, allows to the project managers toanalyze different situations, when they have to definethe most appropriate management policies tooptimize the final values of certain variables forseparate or jointly.Once obtained the management rules,  the projectmanager will decide what rules or rules are theeasiest of applying in function of the concrete projectand of the software organisation in which he isworking.3.     APPLICATION OF FUZZYLOGIC TECHNIQUESThe application of data mining techniques(clustering) and fuzzy logic, will allow us to add twonew improvements in the use of a SPS:1. Obtaining of qualitative management rules,that facilit ates the interpretation of them.2. The project manager doesn't have to definethe results or wanted goals previously.In the figure 4, are shown the steps to obtain this newtype of rules. We can see (Step 1) how the projectmanager doesn't have to define the objectives of theproject. In this case, an algorithm of fuzzy clusteringthat provides qualitative information, processes thedatabase generated by the SPS.This qualitative information that it’s obtained fromthe quantitative data of the project’s attributes, is agroup of fuzzy management rules in graphic format,that allow to the manager to interpret the project’sdata, in a quick and clear way.Next, is described the used algorithm of fuzzyclustering, and are presented the obtained results.3.1   Description of the used algorithmThe fuzzy clustering algorithm that will be applied tothe quantitative data obtained by the simulation ofthe SP, to obtain the qualitative management rules, isbased in the FCM algorithm (Fuzzy C-MeansMethod) (Bezdek 1981). Basically this algorithmtries to classify n elements Xk ∈ X with 1≤ k≤n, withp features each one, that’s to say, X ⊂ ℜp, in C fuzzyclusters, and assigning a membership function µ_i:µ_i_k  : X → [0,1] ∀i  1≤  i ≤ C,  ∀k 1≤  k ≤ nThe fuzzy clustering algorithm developed, followsthe methodology proposed by Sugeno and Yasukawa(Sugeno 1993), that is applied to quantitativedatabases, with an only one-output parameter.Basically it consists on applying the fuzzy partitionFCM to the output parameter. Once obtained apartition of the output space in fuzzy clusters, iscarried out a projection of these clusters in the inputspace, obtaining a fuzzy set in ℜn, that projected oneach axis, assign to each characteristic a fuzzy set.This methodology has been modified and improved,so that it can be applied to database with N outputvariables, also provide a more complete exit. Theimprovements that has been added to the originalmethodology are: The algorithm can work with quantitativedatabases, with N input parameters and Moutput parametersFigure 4 : Steps in the simulation of a project using a SPS                 and fuzzy clusteringProject Simulation D. B. Data MiningTechniques andFuzzy LogicFuzzyManagementRules• Definition of the inter-vals of values of  theparameters5 The different variables can be pondered,assigning weights to calculate of thedistances among points of the space that isbeing partitioned. Other algorithm, to obtain the trapezes ofthe graphic rules processes the obtainedfuzzy sets. The output given by the original FCMalgorithm has been improved, with agraphic interface, that shows the graphs ofthe management rules.The fuzzy management rules obtained by applying tothe database generated by the SPS, the fuzzyclustering algorithm, are represented in the followingway:In this fuzzy rule, the fuzzy set assigned to eachattribute of the SP, is represented by a polyhedron.In the X-axis of each fuzzy set are represented thevalues of the parameter, and in the Y-axis the clustermembership values. This fuzzy management rule canbe read as following:If A1 is low and A2 is medium or highThen O is very low.3.2   Obtained resultsIn the Figure 5 is shown a set of fuzzy managementrules obtained by applying the fuzzy clusteringalgorithm, described previously, to a quantitativedatabase, obtained as result from the simulation of aproject. In this figure are shown the fuzzymanagement rules that were obtained from aquantitative data series, with an only one-outputparameter, in this case, the delivery time of theproject (TIME). With this graphic representation ofthe rules, the project manager has a qualitativeinformation of the SP, easy to interpret, that facilit atehim the decisions making.For example, in the third rule we can see that:“T he delivery time (TIME) takes high values whenthe average daily manpower per staff in the project(DEDIC) is very low, the adaptation delay of thenew technicians (ADEDE) is low, the incorporationdelay of the new technicians to the project (INCDE)is medium-high, and the exit delay of theunnecessary technicians in the project (OUTDE) ishigh” .The rest of rules indicate us the intervals of valuesthat should take the parameters so that thedevelopment time be very low (R1), low (R2) andmedium (R4). This kind of management rules canalso be obtained for quantitative databases with Noutput variables simultaneously, so that the type ofinformation that is provided to the project manager itMin MinMinMax MaxMaxA1 A2 OR1R2R3R4Figure 5 :   Example of fuzzy managment rules with one output parameter (Time)6is much more complete, and it will facilit ate him thedecisions making.As it has been commented previously, with theimprovements that has been carried out to thedescribed fuzzy clustering algorithm, we can use thenumber of input attributes and output variables thatwe need.In the Figure 6 is shown a pair of fuzzy managementrules obtained by applying the fuzzy clusteringalgorithm to a quantitative database, obtained asresult from the simulation of a project with sixattributes and two output variables: cost and time.For example, the second rule (R2) indicates that:“The cost takes medium values and the time takesvery low values simultaneously, when the averagedaily manpower per staff (DEDIC) is medium-low,the dedication to formation (FORMAC) is medium,the infra-estimation of the necessary effort (INFESF)is low, the infra-estimation of the project size(INFREC) is medium-low, the number of techniciansthat begins the project (INFTEC) is medium-highand the medium time of the technicians'incorporation in the project (INCDE) is low” .4.     CONCLUSIONSIn this work have been shown the advantages thatpresent the SPS, based on dynamic models, in frontof the traditional techniques of estimate (most ofstatic character) and the inconveniences that, fromour point of view, present the use of this simulators.These inconveniences are derived of the greatnumber of attributes, so much of the project, as ofthe development organisation that previously shouldbe known to use the simulator.The combined use of a SPS and of machine learningtechniques, as the C4.5, to facilit ate the decisionsmaking (by means of quantitative management rules)in the complex process of software development,allowed us to take an important step in our initialobjective: facilit ate the use of the SPS. Working inthat way, at the moment we are adapting andapplying fuzzy logic techniques to obtain qualitativeinformation about the values of the attributes and theobtained results, of the project. In this case, theproject manager leaving of the initial definition ofthe intervals of values that will t ake the attributes, onthose that has bigger uncertainty, can know the levelof execution of the project objectives.ACKNOWLEDGMENTSThis work has been supported by the Spanishresearch agency CICYT under grant TIC99_0351.REFERENCESAbdel-Hamid T., Madnick S., 1991. "SoftwareProject Dynamics: an integrated approach". Prentice-Hall .Bezdek J.C., 1981. "Pattern Recognition with fuzzyobjective function algorithm". New York : Plenumpress.Quinlan J.R., 1993. "C4.5 Programs for MachineLearning". Morgan Kaufmann Publ. San Francisco.Ramos I., Ruiz M., 1998. "A Reduced DynamicModel to Make Estimations in the Initial Stages of aR1R2Figure 6 : Example of fuzzy mangement rules with two output parameters simultaneously                (Cost and Time)7Software Development Project". In:  Hawkings C etal (ed), INSPIRE III . Process Improvement throughTraining and Education, Pp.:172-185. London.Ramos I., Riquelme J.C., 1999. "The DynamicModels for Software Development Projects and theMachine Learning Techniques". InternationalConference on product focussed software processimprovement. Pp.: 560-574.  Oulu (Finland).Sugeno M., Yasukawa T., 1993. "A Fuzzy-LogicBased approach to qualitative Modeling". IEEETransactions on Fuzzy Systems, Vol.1.Pp.: 7-31.Xiaohong Yuan, et all . 2000. “An application offuzzy clustering to software quality prediction” .Symposium on Application-Specific Systems andSoftware Engineering Technology, Richardson,Texas USA. IEEE Computer Society. In press.Taghi M. Khoshgoftaar, et all . 1999. ” Data miningfor predictiors of software quality” . InternationalJournal of Software Engineering and KnowledgeEngineering. Forthcoming

Improvements In The Decision Making In Software Projects

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Improvements In The Decision Making In Software Projects

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idUS. Depósito de Investigación Universidad de Sevilla