Software maintenance is an important activity in software engineering. Over the decades, software maintenance costs have been continually reported to account for a large majority of software costs [Zelkowitz 1979, Boehm 1981, McKee 1984, Boehm 1988, Erlikh 2000]. This fact is not surprising. On the one hand, software environments and requirements are constantly changing, which lead to new software system upgrades to keep pace with the changes. On the other hand, the economic benefits of software reuse have encouraged the software industry to reuse and enhance the existing systems rather than to build new ones [Boehm 1981, 1999]. Thus, it is crucial for project managers to estimate and manage the software maintenance costs effectively. / Accurate cost estimation of software projects is one of the most desired capabilities in software development Process. Accurate cost estimates not only help the customer make successful investments but also assist the software project manager in coming up with appropriate plans for the project and making reasonable decisions during the project execution. Although there have been reports that software maintenance accounts for the majority of the software total cost, the software estimation research has focused considerably on new development and much less on maintenance. Now if we talk about real time software system(RTSS) development cost estimation and maintenance cost estimation is not much differ from simple software but some critical factor are considered for RTSS development and maintenance like response time of software for input and processing time to give correct output. As like simple software maintenance cost estimation existing models (i.e. Modified COCOMO-II) can be used but after inclusion of some critical parameters related to RTSS. A Hypothetical Expert input and an industry data set of eighty completed software maintenance projects were used to build the model for RTSS maintenance cost. The full model, which was derived through the Bayesian analysis, yields effort estimates within 30% of the actual 51% of the time,outperforming the original COCOMO II model when it was used to estimate theseprojects by 34%. Further performance improvement was obtained when calibrating the full model to each individual program, generating effort estimates within 30% of the actual 80% of the time.

Effective software cost estimation is one of the most challenging and important activities in software development. The software industry does not estimate projects well. Poor estimation leads to poor project planning with resulting schedule overruns, inadequate staffing, low system quality, and many aborted projects. Research on software estimation is needed to build more accurate models of the key aspects of software development. The goals of research in this dissertation are to investigate and improve the modeling of team size and project structures in current software estimation methods.Mathematical models for estimating the impacts of project team size and three variations of project structure are developed. These models accept the outputs of the COCOMO II software estimation tool, allow variation in both team size and project structure, and produce more detailed project estimates. This new extended model of COCOMO II is implemented in a decision support tool f or software estimators called PSEstimate.Following the design science research paradigm, the artifact is evaluated with an experiment with experienced software project managers. Three treatment groups: a manual (no tool) group, a COCOMO II group, and a PSEstimate group, completed two multipart software cost estimation tasks. The accuracy and consistency of the cost and schedule estimates, the participants' confidence in their estimates, and their satisfaction with and perceived usefulness of the cost estimation tool are measured.The experimental results support most of the hypotheses of the dissertation. For most tasks, individuals aided by computer-based decision support tools produce more accurate project effort estimates and are more confident in their estimates than manual estimators. There are no significant differences between the three groups on schedule estimation. A possible explanation is that experienced estimators in the manual group compensate for the inaccuracy of th eir effort estimates by adding time to their schedule estimates.The research contributions are new mathematical models for software estimation based on project team size and structure; a decision support tool (PSEstimate) that incorporates these models; and the experimental results that demonstrate improvements in software estimation by experienced project managers when the new models and tool are applied in practice.

The term “Globalization” brought many challenges with itself in the field of software development. The challenge of accurate effort estimation in GSD is one among them. When talking about effort estimation, the discussion starts for effort estimation methods. There are a number of effort estimation methods available. Existing effort estimation methods used for co-located projects are might not enough capable to estimate effort for distributed projects. This is why; ratio of failure of GSD projects is high. It is important to calibrate existing methods or invent new with respect to GSD environment. This thesis is an attempt to explore the accuracy of effort estimation methods for distributed projects. For this purpose, the authors selected three estimation approaches: COCOMO II, SLIM and ISBSG. COCOMO II and SLIM are two well known effort estimation methods, whereas, ISBSG is used to check the trend of a project depending upon its (ISBSG’s) repository. The selection of the methods and approaches was based on their popularity and advantages over other methods/approaches. Two finished projects from two different organizations were selected and analyzed as case studies. The results indicated that effort estimation with COCOMO II deviated 15.97 % for project A and 9.71% for project B. Whereas, SLIM showed the deviation of 4.17% for project A and 10.86 % for project B. Thus, the authors concluded that both methods underestimated the effort in the studied cases. Furthermore, factors that might cause deviation are discussed and several solutions are recommended. Particularly, the authors state that existing effort estimation methods can be used for GSD projects but they need calibration by considering GSD factors to achieve accurate results. This calibration will help in process improvement of effort estimation.

Odložilík, M. The project of system for electronization of product documentation. Brno: Mendel University, 2009. Thesis deals with design, analysis, estimate and project management of product documentation electronization system with consultations of external enterprise. In theoretical part is gathered theoretical knowledge from literature. Practical part deals with process analysis with sue of BPMN, requirements management, project estimation by method COCOMO II, project management, system design with UML2 diagrams and crating of wireframes. At final part of the thesis is dis-cussed the suitability of designed solution.