Cost estimation in agile development projects

One of the key measures of the resilience of a project is its ability to reach completion on time and on budget, regardless of the turbulent and uncertain environment it may operate within. Cost estimation and tracking are therefore paramount when developing a system. Cost estimation has long been a difficult task in systems development, and although much research has focused on traditional methods, little is known about estimation in the agile method arena. This is ironic given that the reduction of cost and development time is the driving force behind the emergence of the agile method paradigm. This study investigates the applicability of current estimation techniques to more agile development approaches by focusing on four case studies of agile method use across different organisations. The study revealed that estimation inaccuracy was a less frequent occurrence for these companies. The frequency with which estimates are required on agile projects, typically at the beginning of each iteration, meant that the companies found estimation easier than when traditional approaches were used. The main estimation techniques used were expert knowledge and analogy to past projects. A number of recommendations can be drawn from the research: estimation models are not a necessary component of the process; fixed price budgets can prove beneficial for both developers and customers; and experience and past project data should be documented and used to aid the estimation of subsequent projects

Effort estimation Of web based applications

Countless organizations around the world have developed commercial and educational applications for the World Wide Web, the best known example of a hypermedia system. But developing good Web applications is expensive, mostly in terms of time and degree of difficulty for the authors. Our study tries to predict the effort needed for the development of web pages of a particular category, here we have restricted our self to the domain of news web sites. We try to forecast the average effort required to code a page of a new site belonging to the same category based on the analysis of the data available from the existing sites. Here we have considered the data from Top Ten News Sites. The number of pages in the site, Average Number of Lines per Page, Average Number of Scripts per Page, Link Density, and Media Density are taken into account while predicting the effort required to code a page of the site. We consider the effort required to be directly proportional to the number of lines of code. It can be expressed to be in man hours only when we have information about the actual effort in man-hours required to code the site. We use Multiple Linear Regression, Stepwise Regression and Polynomial Regression to analyze the data and obtain the graphs which a be used to predict the approximate effort required to code a web page of a news site. Finally we devised a method to estimate the effort required or the number of lines of code required for a webpage of a news site. These results can be used to devise a standard for the coding of newer news web sites. If they are incorporated in to a web authoring software it would help the author to stick to the guidelines and the standards automatically

An Effort Prediction Framework for Software Defect Correction

Developers apply changes and updates to software systems to adapt to emerging environments and address new requirements. In turn, these changes introduce additional software defects, usually caused by our inability to comprehend the full scope of the modi ed code. As a result, software practitioners have developed tools to aid in the detection and prediction of imminent software defects, in addition to the eort required to correct them. Although software development eort prediction has been in use for many years, research into defect-correction eort prediction is relatively new. The increasing complexity, integration and ubiquitous nature of current software systems has sparked renewed interest in this eld. Eort prediction now plays a critical role in the planning activities of managers. Accurate predictions help corporations budget, plan and distribute available resources eectively and e ciently. In particular, early defect-correction eort predictions could be used by testers to set schedules, and by managers to plan costs and provide earlier feedback to customers about future releases. In this work, we address the problem of predicting the eort needed to resolve a software defect. More speci cally, our study is concerned with defects or issues that are reported on an Issue Tracking System or any other defect repository. Current approaches use one prediction method or technique to produce eort predictions. This approach usually suers from the weaknesses of the chosen prediction method, and consequently the accuracy of the predictions are aected. To address this problem, we present a composite prediction framework. Rather than using one prediction approach for all defects, we propose the use of multiple integrated methods which complement the weaknesses of one another. Our framework is divided into two sub-categories, Similarity-Score Dependent and Similarity-Score Independent. The Similarity-Score Dependent method utilizes the power of Case-Based Reasoning, also known as Instance-Based Reasoning, to compute predictions. It relies on matching target issues to similar historical cases, then combines their known eort for an informed estimate. On the other hand, the Similarity-Score Independent method makes use of other defect-related information with some statistical manipulation to produce the required estimate. To measure similarity between defects, some method of distance calculation must be used. In some cases, this method might produce misleading results due to observed inconsistencies in history, and the fact that current similarity-scoring techniques cannot account for all the variability in the data. In this case, the Similarity-Score Independent method can be used to estimate the eort, where the eect of such inconsistencies can be reduced. We have performed a number of experimental studies on the proposed framework to assess the eectiveness of the presented techniques. We extracted the data sets from an operational Issue Tracking System in order to test the validity of the model on real project data. These studies involved the development of multiple tools in both the Java programming language and PHP, each for a certain stage of data analysis and manipulation. The results show that our proposed approach produces signi cant improvements when compared to current methods

Estimació de l’esforç per a la realització de pressupostos en la producció d’aplicacions multimèdia de formació semipresencial (blended learning)

Aquesta tesi s'emmarca en l'estimació de l'esforç per al desenvolupament de projectes multimèdia de formació semipresencial. En la producció multimèdia, és necessari poder fer una previsió tant de calendari com de pressupost en estadis molt inicials del projecte. Per al càlcul del pressupost cal especificar quines tasques es duran a terme, quins recursos seran necessaris i el cost d'aquestes tasques i recursos. Una manera de calcular el cost total d'una tasca és multiplicar el cost d'una unitat de temps de la tasca pel temps invertit en desenvolupar-la: l'esforç. Aquest esforç, que és clau per al càlcul del pressupost, només se sap del cert en finalitzar el projecte. El que interessa, però, és poder-lo conèixer a priori, per això se'n fan estimacions. Com en tants d'altres aspectes, l'entorn multmèdia deu a l'entorn del desenvolupament del software les bases de l'estimació d'esforços, on en els últims 15 anys s'han establert moltes comparacions en enginyeria del software entre diverses tècniques de predicció, segons la seva exactitud. En el marc de la producció web, els estudis publicats s'han concentrat més en proposar mètodes, metodologies i eines per als processos bàsics i per augmentar la qualitat del producte, que no pas en models de càlcul de l'esforç. I els que han estat publicats en aquest sentit se centren en estimació del temps total per al desenvolupament d'aplicacions web (hipermèdia o software). Amb aquest treball es pretén aprofundir sobre com fer estimacions de l'esforç per a un projecte multimèdia de formació semipresencial. El temps total estimat ha de servir per poder calcular el pressupost i els temps parcials per tasca han de servir per formar un equip amb el perfil indicat i fer una acurada assignació de tasques. A diferència de la majoria d'estudis publicats, les dades que s'estudien en aquest treball provenen de projectes reals amb orientació professional. Les dades de producció d'aquest treball estan ordenades per produccions i projectes i són de tres tipus: a) dades de caracterització del projecte i de les seves produccions; b) dades que dimensionen el projecte i/o la producció; c) temps invertit en el projecte i/o producció (o dedicacions), segons el tipus de tasca que s'ha desenvolupat. El registre de les dades s'ha dut a terme amb dos sistemes diferents: 1) De 1999 a l'abril de 2001: sistema basat en fulls de càlcul d'Excel, emmagatzemats en local i gestionats manualment; 2) De l'abril de 2001 fins a juliol de 2004: sistema basat en asp i Acces, amb accés on-line a través d'una intranet i un alt grau d'automatització en la gestió. Les anàlisis aplicades han estat les de regressió lineal de mínims quadrats ordinaris, prenent com a model de regressió teòric en consonància amb el marc teòric de la investigació: VariableTemps = a + b1 VariabreTemps1 + b2 VariableTemps2 Per a comprovar el poder de predicció de la tècnica aplicada s'han utlitzat els mètodes més comuns dels valors d'MMRE i Pred(25), però, també s'han observat els diagrames de caixes dels residus. Per determinar si i ha diferències entre les tècniques utilitzades, s'han comparat els residus amb tests com el de Mann-Whitney. Es consideren àmpliament assolits els objectius de la investigació, entre els quals es troben, com a més importants: a) tipificar les diferents produccions dutes a terme al LAM; b) Confirmar que es poden fer estimacions d'esforços a partir de variables de dimensionat recollides al LAM per als projectes de formació semipresencial. Com a línies de treball futur es proposen: a) ampliar els resultats d'aquest treball amb la consideració de variables predictores que no s'han tingut en compte; b) obtenir més bons resultats per a temps parcials; c) obtenir resultats per a produccions en les quals no s'han pogut dur a terme les anàlisis, tot incloent altres variables; d) aplicar altres tècniques d'estimació al mateix tipus de dades.Postprint (published version

Boletín Oficial de la Provincia de Oviedo: Número 28 - 1929 febrero 4

Software development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Applying machine learning algorithms alone often can not achieve satisfactory results. In this paper, we present an integrated data mining framework that incorporates domain knowledge into a series of data analysis and modeling processes, including visualization, feature selection, and model validation. An empirical study on the software effort estimation problem using a benchmark dataset shows the effectiveness of the proposed approach.Unpublished[1] L. Cao and C. Zhang, “The evolution of kdd: towards domain-driven data mining,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 21, pp. 677–692, 2007. [2] S. Chulani, B. Boehm, and B. Steece, “Bayesian analysis of empirical software engineering cost models,” IEEE Transactions on Software Engineering, vol. 25, no. 4, 1999. [3] S. Conte, H. Dunsmore, and H. Shen, Software engineering metrics and models. Benjamin/Cummings, 1986. [4] J. J. Cuadrado-Gallego, M.-A. Sicilia, M. Garre, and D. Rodriguez, “An empirical study of process-related attributes in segmented software cost-estimation relationships,” The Journal of Systems and Software, vol. 79, pp. 353–361, 2006. [5] J. Hale, A. Parrish, B. Dixon, and R. Smith, “Enhancing the Cocomo estimation models,,” IEEE Software, vol. 17, pp. 45–49, 2000. [6] R. Jeffery, M. Ruhe, and I. Wieczorek, “Using public domain metrics to estimate software development effort,” in Proc. of 7th IEEE Symposium on Software Metrics, 2001, pp. 16–27. [7] K. Kira and L. A. Rendell, “A practical approach to feature selection,” in Proceedings of International Conference on Machine Learning, 1992, pp. 249–256. [8] Q. Liu and R. Mintram, “Preliminary data analysis methods in software estimation,” Software quality journal, vol. 13, pp. 91–115, 2005. [9] C. Mair, G. Kododa, M. Lefley, K. Phalp, C. Schofield, M. Shepperd, and S. Webster, “An investigation of machine learning based prediction systems,” System and Software, vol. 53, pp. 23–29, 2000. [10] E. Mendes, I. Watson, T. C., N. Mosley, and S. Counsell, “A comparison of development effort estimation techniques for web hypermedia applications,” in Proc. of 8th IEEE Symposium on Software Metrics, 2002, pp. 131–140. [11] T. Menzies, D. Port, Z. Chen, and J. Hihn, “Simple software cost analysis: safe or unsafe?” in PROMISE ’05: Proceedings of the 2005 workshop on Predictor models in software engineering. New York, NY, USA: ACM Press, 2005, pp. 1–6. [12] S. Oligny, P. Bourque, A. Abran, and B. Fournier, “Exploring the relation between effort and duration in software engineering projects,” in Proceedings of World Computer Congress 2000, 2000, pp. 175–178. [13] Y. Ou, L. Cao, C. Luo, and C. Zhang, “Domain-driven local exceptional pattern mining for detecting stock price manipulation,” in PRICAI 2008: Trends in Artificial Intelligence, 2008, pp. 849–858. [14] C. Pohle, “Integrating and updating domain knowledge with data mining,” in Proceedings of the VLDB 2003 PhD Workshop (Electronic Ed.), M. Scholl and T. Grust, Eds., 2003. [15] M. Robnik-Sikonja and I. Kononenko, “An adaptation of relief for attribute estimation in regression,” in ICML ’97: Proceedings of the Fourteenth International Conference on Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1997, pp. 296–304. [16] W. Sammon, “A nonlinear mapping for data analysis,” IEEE Transactions on Computers, vol. 5, pp. 401–409, 1969. [17] J. Sayyad Shirabad and T. Menzies, “The PROMISE Repository of Software Engineering Databases.” School of Information Technology and Engineering, University of Ottawa, Canada. http://promise.site.uottawa.ca/SERepository., 2005. [Online]. Available: http://promise.site.uottawa.ca/SERepository [18] M. Shepperd and C. Schofield, “Estimating software project effort using analogies,” IEEE Transactions on Software Engineering, vol. 23, no. 12, pp. 736–743, 1997. [19] J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, 2000. [20] M. Siadaty and W. Knaus, “Locating previously unknown patterns in data-mining results: a dual data- and knowledge-mining method,” BMC Medical Informatics and Decision Making, vol. 6, no. 1, p. 13, 2006. [Online]. Available: http://www.biomedcentral.com/1472- 6947/6/13 [21] A. P. Sinha and H. Zhao, “Incorporating domain knowledge into data mining classifiers: An application in indirect lending,” Decision Support Systems, vol. 46, no. 1, pp. 287 – 299, 2008. [22] K. Srinivasan and D. Fisher, “Machine learning approaches to estimating software development effort,” IEEE Transaction on Software Engineering, vol. 21, pp. 126–137, 1995. [23] I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, 2nd ed. Morgan Kaufmann, 2005