This paper aims to provide a basis for the improvement of software-estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A Web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) increase the breadth of the search for relevant studies, 2) search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) conduct more studies on estimation methods commonly used by the software industry, and 4) increase the awareness of how properties of the data sets impact the results when evaluating estimation methods

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A Systematic Review of Software Development Cost Estimation Studies

Basics of Software Engineering Experimentation is a practical guide to experimentation in a field which has long been underpinned by suppositions, assumptions, speculations and beliefs It demonstrates to software engineers how Experimental Design and Analysis can be used to validate their beliefs and ideas The book does not assume its readers have an in-depth knowledge of mathematics, specifying the conceptual essence of the techniques to use in the design and analysis of experiments and keeping the mathematical calculations clear and simple Basics of Software Engineering Experimentation is practically oriented and is specially written for software engineers, all the examples being based on real and fictitious software engineering experiments

Basics of Software Engineering Experimentation

http://unbox.org/all/trunk/doc/06/costs/jorgensen04.pdf

A review of studies on expert estimation of software development effort

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Software process simulation modeling: Why? What? How?

During software development, projects often experience risky situations. If projects fail to detect such risks, they may exhibit confused behavior. In this paper, we propose a new scheme for characterization of the level of confusion exhibited by projects based on an empirical questionnaire. First, we designed a questionnaire from five project viewpoints, requirements, estimates, planning, team organization, and project management activities. Each of these viewpoints was assessed using questions in which experience and knowledge of software risks are determined. Secondly, we classify projects into "confused" and "not confused," using the resulting metrics data. We thirdly analyzed the relationship between responses to the questionnaire and the degree of confusion of the projects using logistic regression analysis and constructing a model to characterize confused projects. The experimental result used actual project data shows that 28 projects out of 32 were characterized correctly. As a result, we concluded that the characterization of confused projects was successful. Furthermore, we applied the constructed model to data from other projects in order to detect risky projects. The result of the application of this concept showed that 7 out of 8 projects were classified correctly. Therefore, we concluded that the proposed scheme is also applicable to the detection of risky projects.

An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis

In this paper, we propose a new model for software projects and an estimation method for the quality, cost and delivery date. The new model consists of Project model and Process model. Project model focuses on three key components: activity, product and developer of the project. Process model includes a set of Activity models, each of which specifies design, coding, review, test, and debug activities respectively using GSPN. Moreover, the new model can take the influence of human factors into account by introducing the concept of "workload". Next, we develop a simulator which supports description of the target process, executes the process described by Activity model and analyses the simulation results statistically. Then, we apply the simulator to real software projects at certain organization and compare the estimated values with actual data. The experimental results show the applicability of the proposed simulator to manage real software project in the future.

A new software project simulator based on generalized stochastic Petri-net

The paper statistically analyzes the relationship between review and software quality and the relationship between review and productivity, utilizing software metrics on 36 actual projects executed in the OMRON Corporation from 1992 to 1994. Firstly, by examining the relationship between review effort and field quality (the number of faults after delivery) of each project, and the relationship between the number of faults detected in review and field quality of each project, we reasoned that: (1) greater review effort helps to increase field quality (decrease the number of faults after delivery); (2) source code review is more effective in order to increase field quality than design review; (3) if more than 10% of total design and programming effort is spent on review, one can achieve a quite stable field quality. We noticed that no relevant effects were recognized in productivity (LOC/staff month) with respect to a review rate of up to 20%. As a result of the analysis above, we recommended that 15% of review effort is a suitable percentage to use as a guideline for our software project management.

Analysis of review's effectiveness based on software metrics

Software organizations and projects need guidance on how to improve software process, not just guidelines on what to improve Several surveys demonstrate that the Capability Maturity Model (CMM) and ISO-9000 only provide the latter We report our in-depth analysis on a seventeen-month effort in software process improvement (SPI) at OMRON Corporation The goal of the analysis was to identify issues and challenges of SPI and to design a step-wise practical method to avoid such problems Major problems we have found include the lack of shared goal among stakeholders, insufficient understanding of the current progress of SPI efforts, and underutilization of a large amount of complex information generated during SPI We present the method for software organizations and projects for dealing with the problems, and argue for a knowledge-based SPI support system based on the method

Toward computational support for software process improvement activities

During the process of software development, senior managers often find indications that projects are risky and take appropriate actions to recover them from this dangerous status. If senior managers fail to detect such risks, it is possible that such projects may collapse completely. In this paper, we propose a new scheme for the characterization of risky projects based on an evaluation by the project manager. In order to acquire the relevant data to make such an assessment, we first designed a questionnaire from five view-points within the projects: requirements, estimations, team organization, planning capability and project management activities. Each of these viewpoints consisted of a number of concrete questions. We then analyzed the responses to the questionnaires as provided by project managers by applying a logistic regression analysis. That is, we determined the coefficients of the logistic model from a set of the questionnaire responses. The experimental results using actual project data in Company A showed that 27 projects out of 32 were predicted correctly. Thus we would expect that the proposed characterizing scheme is the first step toward predicting which projects are risky at an early phase of the development.

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Yasunari Takagi

Papers

An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis

A new software project simulator based on generalized stochastic Petri-net

Analysis of review's effectiveness based on software metrics

Toward computational support for software process improvement activities

Characterization of risky projects based on project managers' evaluation