A model for estimating the size of a formal communication protocol specification and its implementation

Good project management is key when developing a software system successfully. To manage a project well, it is important to have the optimal resource allocation which is affected by the size of an implementation. Early software size estimation is essential for good project management. Existing software size models estimate the size of an implementation usually in terms of the number of lines of code. The main drawback of these models is that there is a wide margin of uncertainty as the actual size depends on the type of application and the software development method adopted. To address this drawback, we focus our work on communication protocol, and propose that the size of a formal specification needs to be estimated from an informal specification. This paper presents a two-stage size model for estimating the sizes of a formal communication protocol specification and its implementation, with the model validated using a test data set. The main benefit of this work is that it can give an indication of the likely sizes of both a formal specification and its implementation early at the development stage, giving developers a technique for managing communication software project better.     

Using protean for verifying a complex protocol — A case study

Communication protocols used in the field have always suffered from failures. Some of these faults are the result of design errors; others are the result of implementation errors. These errors dramatically increase maintenance cost and decrease software reliability. The best time to maintain software is during its design stage. A formal approach to developing a protocol is deemed necessary to improve the quality of communication software and to reduce maintenance costs. Protocol verification plays a major role in achieving these objectives; a protocol is first specified formally and then this formal specification is analysed using a computer-aided tool. PROTEAN is a software tool that verifies a protocol specified formally in Numerical Petri Nets. This paper describes the experience and practice of using PROTEAN to verify a complex protocol and then presents an evaluation of PROTEAN and its associated techniques in the light of protocol software development. The ISO FTAM protocol is used as a case study.     

PSAMS: a communication protocol specification assessment and measurement system

An obstacle to the uses of software metrics and size models, which we have developed for measuring the complexity and maintainability of a communication protocol specified in Estelle and for estimating the size of its specification and implementation, is the time‐consuming effort in collecting the metrics. To address this problem, a software system called PSAMS (protocol specification assessment and measurement system) for automatically calculating the metrics and sizes of specification and implementation has been developed. This paper describes the design of PSAMS, which provides five functionalities for a communication protocol Estelle specification: exploring its specification, measuring its complexity, assessing its maintainability, estimating its specification size and estimating its implementation size. To demonstrate the usefulness of PSAMS, we have applied it to measure the complexity and maintainability of 10 communication protocol Estelle specifications; the measurement results and decision support information provided by each functionality are presented in this paper. With PSAMS, communication protocol designers and developers are able to assess the complexity of a communication protocol early in the specification stage and have information which helps them manage a communication software project better. Copyright © 2002 John Wiley & Sons, Ltd.     

An investigation of artificial neural networks based prediction systems in software project management

A critical issue in software project management is the accurate estimation of size, effort, resources, cost, and time spent in the development process. Underestimates may lead to time pressures that may compromise full functional development and the software testing process. Likewise, overestimates can result in noncompetitive budgets. In this paper, artificial neural network and stepwise regression based predictive models are investigated, aiming at offering alternative methods for those who do not believe in estimation models. The results presented in this paper compare the performance of both methods and indicate that these techniques are competitive with the APF, SLIM, and COCOMO methods.     

A new perspective on data homogeneity in software cost estimation: a study in the embedded systems domain

Cost estimation and effort allocation are the key challenges for successful project planning and management in software development. Therefore, both industry and the research community have been working on various models and techniques to accurately predict the cost of projects. Recently, researchers have started debating whether the prediction performance depends on the structure of data rather than the models used. In this article, we focus on a new aspect of data homogeneity, “cross- versus within-application domain”, and investigate what kind of training data should be used for software cost estimation in the embedded systems domain. In addition, we try to find out the effect of training dataset size on the prediction performance. Based on our empirical results, we conclude that it is better to use cross-domain data for embedded software cost estimation and the optimum training data size depends on the method used.     

A comparison of software project overruns - flexible versus sequential development models

Flexible software development models, e.g., evolutionary and incremental models, have become increasingly popular. Advocates claim that among the benefits of using these models is reduced overruns, which is one of the main challenges of software project management. This paper describes an in-depth survey of software development projects. The results support the claim that projects which employ a flexible development model experience less effort overruns than do those which employ a sequential model. The reason for the difference is not obvious. We found, for example, no variation in project size, estimation process, or delivered proportion of planned functionality between projects applying different types of development model. When the managers were asked to provide reasons for software overruns and/or estimation accuracy, the largest difference was that more of flexible projects than sequential projects cited good requirement specifications-and good collaboration/communication with clients as contributing to accurate estimates.     

The role of distances in requirements communication: a case study

Requirements communication plays a vital role in development projects in coordinating the customers, the business roles and the software engineers. Communication gaps represent a significant source of project failures and overruns. For example, misunderstood or uncommunicated requirements can lead to software that does not meet the customers’ requirements, and subsequent low number of sales or additional cost required to redo the implementation. We propose that requirements engineering (RE) distance measures are useful for locating gaps in requirements communication and for improving on development practice. In this paper, we present a case study of one software development project to evaluate this proposition. Thirteen RE distances were measured including geographical and cognitive distances between project members, and semantic distances between requirements and testing artefacts. The findings confirm that RE distances impact requirements communication and project coordination. Furthermore, the concept of distances was found to enable constructive group reflection on communication gaps and improvements to development practices. The insights reported in this paper can provide practitioners with an increased awareness of distances and their impact. Furthermore, the results provide a stepping stone for further research into RE distances and methods for improving on software development processes and practices.

     

Software development cost estimation: Integrating neural network with cluster analysis

For software project planning control and management, an accurate estimate of software development cost is important. Past research has focused on using parametric models to predict development cost based on attributes such as lines of code or function points. This requires researchers to identify the set of factors that influence cost estimation before the system is constructed. We propose a non-parametric approach that integrates a neural network method with cluster analysis to estimate development cost. The integration of the two techniques not only allows for a more accurate cost estimate but also leads to an increase in the training efficacy of the network.     

Visual comparison of software cost estimation models by regression error characteristic analysis

The well-balanced management of a software project is a critical task accomplished at the early stages of the development process. Due to this requirement, a wide variety of prediction methods has been introduced in order to identify the best strategy for software cost estimation. The selection of the best technique is usually based on measures of error whereas in more recent studies researchers use formal statistical procedures. The former approach can lead to unstable and erroneous results due to the existence of outlying points whereas the latter cannot be easily presented to non-experts and has to be carried out by an expert with statistical background. In this paper, we introduce the regression error characteristic (REC) analysis, a powerful visualization tool with interesting geometrical properties, in order to validate and compare different prediction models easily, by a simple inspection of a graph. Moreover, we propose a formal framework covering different aspects of the estimation process such as the calibration of the prediction methodology, the identification of factors that affect the error, the investigation of errors on certain ranges of the actual cost and the examination of the distribution of the cost for certain errors. Application of REC analysis to the ISBSG10 dataset for comparing estimation by analogy and linear regression illustrates the benefits and the significant information obtained.     

Software project development cost estimation

This paper reports the results of an empirical investigation of the relationships between effort expended, time scales, and project size for software project development. The observed relationships were compared with those predicted by Lawrence Putnam's Rayleigh curve model and Barry Boehm's COCOMO model. The results suggested that although the form of the basic empirical relationships were consistent with the cost models, the COCOMO model was a poor estimator of cost for the current data set and the data did not follow the Rayleigh curve suggested by Putnam. However, the results did suggest that it was possible to develop cost models tailored to a particular environment and to improve the precision of the models as they are used during the development cycle by including additional information such as the known effort for the early development phases. The paper finishes by discussing some of the problems involved in developing useful cost models.     

Achieving dependability throughout the development process: adistributed software experiment

Distributed software engineering techniques and methods for improving the specification and testing phases are considered. To examine these issues, an experiment was performed using the design diversity approach in the specification, design, implementation, and testing of distributed software. In the experiment, three diverse formal specifications were used to produce multiple independent implementations of a distributed communication protocol in Ada. The problems encountered in building complex concurrent processing systems in Ada were also studied. Many pitfalls were discovered in mapping the formal specifications into Ada implementations     

Identifying factors affecting software development cost and productivity

Software systems of today are often complex, making development costs difficult to estimate. This paper uses data from 50 projects performed at one of the largest banks in Sweden to identify factors that have an impact on software development cost. Correlation analysis of the relationship between factor states and project costs was assessed using ANOVA and regression analysis. Ten out of the original 31 factors turned out to have an impact on software development project cost at the Swedish bank including the: number of function points, involved risk, number of budget revisions, primary platform, project priority, commissioning body’s unit, commissioning body, number of project participants, project duration, and number of consultants. In order to be able to compare projects of different size and complexity, this study also considers the software development productivity defined as the amount of function points per working hour in a project. The study at the bank indicates that the productivity is affected by factors such as performance of estimation and prognosis efforts, project type, number of budget revisions, existence of testing conductor, presentation interface, and number of project participants. A discussion addressing how the productivity factors relate to cost estimation models and their factors is presented. Some of the factors found to have an impact on cost are already included in estimation models such as COCOMO II, TEAMATe, and SEER-SEM, for instance function points and software platform. Thus, this paper validates these well-known factors for cost estimation. However, several of the factors found in this study are not included in established models for software development cost estimation. Thus, this paper also provides indications for possible extensions of these models.     

An experimental parallel system (EPS)

This paper reports on the initial stages of a research project involving the development of an experimental parallel computing system. The system is intended to support research in a variety of areas, including computer architectures, task models, language implementations and interconnection techniques. A pragmatic approach is being undertaken to evaluating techniques in these areas through applying the system to actual applications.

The system is based on a hierarchical structure of processors, using shared memory as the communication medium. This paper identifies the main features of the hardware being used, and presents an outline of the initial software for task creation and management.     

A Study of Reasoning Processes in Software Maintenance Management

More resources are spent on maintaining software than for its development. Maintenance costs for large scale software systems can amount to somewhere between 40 and 67% of the total system life cycle cost. It is therefore important to manage maintenance costs, and to balance costs with benefits. Frequently this task is approached, at least in the literature, merely as a software cost estimation problem. Unfortunately, the creation of effort estimation models for maintenance – a primary requisite for cost calculation – has not yet been satisfactorily addressed. At the same time, project managers do not estimate costs first, but instead prioritize maintenance projects, trying to determine which projects to carry out (first) within their fixed budgets and resource capabilities. This essentially means that cost estimation is done qualitatively first before formal cost estimation techniques are employed. Recognizing the problems associated with standard, regression based estimation models, and focusing on the needs of software project managers, this research studied the process of project prioritization as an expert problem solving and decision making task, through concurrently taken (think aloud) protocols. Analysis of these protocols revealed that experts rarely make use of formal mathematical models to determine project priorities or resource needs, such as COCOMO or FPA, although project size is a key determinant of a project's priority. Instead, estimators qualitatively consider cost or value, urgency, and difficulty of a maintenance task, then prioritize projects accordingly, followed by a decision concerning further treatment of the problem. The process employs case based reasoning and the use of heuristics. While different experts may use different strategies, there exists great overlap in their overall prioritization procedure.     

Software size estimation of object-oriented systems

The strengths and weaknesses of existing size estimation techniques are discussed. The nature of software size estimation is considered. The proposed method takes advantage of a characteristic of object-oriented systems, the natural correspondence between specification and implementation, in order to enable users to come up with better size estimates at early stages of the software development cycle. Through a statistical approach the method also provides a confidence interval for the derived size estimates. The relation between the presented software sizing model and project cost estimation is also considered     

Evidence-based guidelines for assessment of software development cost uncertainty

Several studies suggest that uncertainty assessments of software development costs are strongly biased toward overconfidence, i.e., that software cost estimates typically are believed to be more accurate than they really are. This overconfidence may lead to poor project planning. As a means of improving cost uncertainty assessments, we provide evidence-based guidelines for how to assess software development cost uncertainty, based on results from relevant empirical studies. The general guidelines provided are: 1) Do not rely solely on unaided, intuition-based uncertainty assessment processes, 2) do not replace expert judgment with formal uncertainty assessment models, 3) apply structured and explicit judgment-based processes, 4) apply strategies based on an outside view of the project, 5) combine uncertainty assessments from different sources through group work, not through mechanical combination, 6) use motivational mechanisms with care and only if greater effort is likely to lead to improved assessments, and 7) frame the assessment problem to fit the structure of the relevant uncertainty information and the assessment process. These guidelines are preliminary and should be updated in response to new evidence.     

Improving risk management: moving from risk elimination to risk avoidance

This study has examined software reliability using a risk management framework to analyze popular risk management models with regard to their emphasis on risk avoidance. Too often, risk reduction occurs late in the software development life cycle when changes are costly and less effective. We suggest that early risk avoidance techniques, like the cleanroom software development process and its sub-process of software inspections, leads to superior products. Our approach is different from the current mainstream approach of testing to eliminate errors because we propose using risk models that emphasize preventive risk management early in development. We argue that the use of risk avoidance leads to benefits that far outweigh the cost of implementation.