A software development process modeling framework is constructed under the formalism of state modeling. The approach interconnects instances of a general development-activity model into a composite system. Simulation results are presented, and implications for control-theoretic decision support are briefly discussed.

Optimal control of robotic systems with logical constraints is an instance of a hybrid optimal control problem. It has been traditionally treated as a mixed-integer programming problem (MIP) which is of combinatorial complexity. This paper proposes a new approach for transforming logical constraints into inequality and equality constraints involving only continuous variables. In this way the hybrid optimal control problem is converted to a smooth optimal control problem that can in turn be solved using traditional nonlinear programming methods, thereby dramatically reducing the computational complexity of finding the solution. We illustrate the techniques by solving an optimal path planning problem for multiple unmanned aerial vehicles (UAVs) with collision avoidance. Simulation results are given to show the effectiveness of the approach.

This paper studies the problem of stabilizing wheeled mobile robots (WMRs) subject to wheel slippage to a predefined set. When slippage of the wheels can occur, WMRs can be modeled as hybrid systems. Model predictive control for such systems typically results in numerical methods of combinatorial complexity. We show that recently developed embedding techniques can be used to formulate numerical algorithms for the hybrid model predictive control (MPC) problem that have the same complexity as the MPC for smooth systems. We also discuss in detail the numerical techniques that lead to efficient and robust MPC algorithms. Examples are given to illustrate the effectiveness of the approach.

A quantitative, adaptive process control technique is described using an industrially validated model of the software system test phase as concrete target to be controlled. The technique combines the use of parameter correction and model predictive control to overcome the problems induced by modeling errors, parameter estimation errors, and limits on the resources available for productivity improvement.

A data collection and grooming tool named DIG is reported. The tool allows the management of a software process to be able to extract data from a variety of project repositories, groom the data, and generate reports. The reports are aimed at assisting the management in the control of the software development process. DIG allows management to build a complete representation of the productivity data in order to better tease out the parameters and understand the causes of variation in the data. In addition, the data obtained and groomed by DIG may be used to calibrate and apply process control models.

We describe two case studies to investigate the application of a state variable model to control the system test phase of software products. The model consists of two components: a feedback control portion and a model parameter estimation portion. The focus in this study is on the assessment of the goodness of the estimates and predictions of the model parameters and their utility in the management of the system test phase. Two large network management applications developed and tested at Sun Microsystems served as the subjects in these studies. Unlike the release of products based on marketing or deadline pressure, estimates of the number of residual defects are used to control the quality of the product being released. The estimates of the number of defects in the application when the test phase began and at the current checkpoint are obtained. In addition a prediction is made regarding the reduction in the number of remaining defects over the remaining period. The estimates and predictions assist the management in planning the test phase and allow inferring the level of customer support needed subsequent to product release. The results of both case studies are satisfactory and, when viewed in light of other studies conducted at Sun Microsystems, show the applicability of the state variable model to the management of the software test process.

Statistical Process Control (SPC) is a powerful tool to control the quality of processes. It assists management personnel in the identification of problems and actions to be taken to bring a process into a stable state. SPC has been applied in various fields, including the Software Development Process. However, some processes are better characterized by factors that exhibit an exponential behavior. The use of such factors for process control limits the application of traditional SPC techniques. The Software Test Process (STP) characterized by the decay in the number of remaining errors, failure intensity, and an increase in code coverage, is one such process.A variant of the traditional SPC technique is proposed. This variant uses logarithmic transformation to allow the statistical control of processes whose dominant behavior is best described by an exponential. An evaluation of the proposed transformation carried out using simulation and a case study from an industrial project, encourages the application of the proposed variant to the STP.

We report on the sensitivity analysis of a state variable model (Model S) proposed earlier. Model S captures the dominant behavior of the system test phase of the software test process. Sensitivity analysis is a mathematical methodology to compute changes in the system behavior due to changes in system parameters or variables. This is particularly important when parameters are calibrated using noisy or small data sets. Nevertheless, by mathematically quantifying the effects of parameter variations on the behavior of the model, and thereby the STP, one can easily and quickly evaluate the effect of such variations on the process performance without having to perform extensive simulations. In all cases studied, model S behaved according to empirical observations which serves to validate the model. It is also shown that sensitivity analysis can suggest structural improvements in a model when the model does not behave as expected.

We report a study to determine the impact of four types of disturbances on the failure intensity of a software product undergoing system test. Hardware failures, discovery of a critical fault, attrition in the test team, are examples of disturbances that will likely affect the convergence of the failure intensity to its desired value. Such disturbances are modeled as impulse, pulse, step, and white noise. Our study examined, in quantitative terms, the impact of such disturbances on the convergence behavior of the failure intensity. Results from this study reveal that the behavior of the state model, proposed elsewhere, is consistent with what one might predict. The model is useful in that it provides a quantitative measure of the delay one can expect when a disturbance occurs.

A novel approach to model the system test phase of the software life cycle is presented. This approach is based on concepts and techniques from control theory and is useful in computing the effort required to reduce the number of errors and the schedule slippage under a changing process environment. Results from these computations are used, and possibly revised, at specific checkpoints in a feedback-control structure to meet the schedule and quality objectives. Two case studies were conducted to study the behavior of the proposed model. One study reported here uses data from a commercial project. The outcome from these two studies suggests that the proposed model might well be the first significant milestone along the road to a formal and practical theory of software process control.

A closed-loop feedback control model of the software test process (STP) is described. The model is grounded in the well established theory of automatic control. It offers a formal and novel procedure for using product reliability or failure intensity as a basis for closed loop control of the STP. The reliability or the failure intensity of the product is compared against the desired reliability at each checkpoint and the difference fed back to a controller. The controller uses this difference to compute changes necessary in the process parameters to meet the reliability, or failure intensity objective at the terminal checkpoint (the deadline). The STP continues beyond a checkpoint with a revised set of parameters. This procedure is repeated at each checkpoint until the termination of the STP. The procedure accounts for the possibility of changes (during testing), in reliability or failure intensity objective, the checkpoints, and the parameters that characterize the STP. The effectiveness of this procedure was studied using commercial data available in the public domain and also from the data generated through simulation. In all cases, the use of feedback control produces adequate results allowing the achievement of the objectives.

A model is proposed to assist software test managers in controlling the behavior and progress of the Software Test Process (STP) by allowing them to compare predicted behavior against observed progress made at various checkpoints. The model, whose parameters are based on measured data and process characteristics, generates the predicted behavior. An algorithm for the parameter estimation is set forth. The error between the predicted and desired behavior is used to drive a parametric control algorithm that tells the manager how to correct for schedule deviations.

The paper reports the results of a sensitivity analysis of a state variable model of the Software Test Process (STP). Given a state model of the STP, a sensitivity matrix is calculated using tensor algebra. The sensitivity matrix allows computation of output variations to small perturbations in the model parameters. The results confirm that the model behaves in a manner very similar to what one might expect from a software test process. Results of this analysis also suggest changes and enhancements in the model to improve its accuracy in predicting the behavior of the Software Test Process.

This paper introduces the concept of a hybrid system and some of the challenges associated with the stability of such systems, including the issues of guaranteeing stability of switched stable systems and finding conditions for the existence of switched controllers for stabilizing switched unstable systems. In this endeavour, this paper surveys the major results in the (Lyapunov) stability of finite-dimensional hybrid systems and then discusses the stronger, more specialized results of switched linear (stable and unstable) systems. A section detailing how some of the results can be formulated as linear matrix inequalities is given. Stability analyses on the regulation of the angle of attack of an aircraft and on the PI control of a vehicle with an automatic transmission are given. Other examples are included to illustrate various results in this paper.

The design of variable-structure control (VSC) systems for a class of multivariable, nonlinear, time-varying systems is presented. Using the Utkin-Drazenovic method of equivalent control and generalized Lyapunov stability concepts, the VSC design is described in a unified manner. Complications that arise due to multiple inputs are examined, and several approaches useful in overcoming them are developed. Recent developments are investigated, as is the kinship of VSC and the deterministic approach to the control of uncertain systems. All points are illustrated by numerical examples. The recent literature on VSC applications is surveyed.>

This paper surveys in a tutorial fashion theoretical and applications aspects of the continuation method for the solution of large scale system engineering problems. The continuation method is motivated and defined. The existence of the method is formulated in terms of degree theory a la Algebraic Topology. Examples are given throughout the paper and applications to engineering and economic problems are cited and described.