一种新的基于不完全排错的最优软件发行策略

软件的最优发行管理问题是软件可靠性研究的一个关键问题.现有的最优软件发行模型大都假定软件排错过程是完全的,并且在排错过程中没有新的故障引入,这种假设在很多情况下是不合理的.本文提出了一种新的最优软件发行管理模型,该模型既考虑了软件的不完全排错过程,又考虑了在排错过程中可能会引入新的故障,同时还考虑了由于排错经验的不断积累,软件的完全排错概率会增加的情况.本文同时给出了该模型的解.     

Markovian Imperfect Software Debugging Model and Its Performance Measures

Abstract

This paper considers a Markovian imperfect software debugging model incorporating two types of faults and derives several measures including the first passage time distribution. When a debugging process upon each failure is completed, the fault which causes the failure is either removed from the fault contents with probability p or is remained in the system with probability 1 ? p. By defining the transition probabilities for the debugging process, we derive the distribution of first passage time to a prespecified number of fault removals and evaluate the expected numbers of perfect debuggings and debugging completions up to a specified time. The availability function of a software system, which is the probability that the software is in working state at a given time, is also derived and thus, the availability and working probability of the software system are obtained. Throughout the paper, the length of debugging time is treated to be random and thus its distribution is assumed. Numerical examples are provided for illustrative purposes.     

An optimal software debugging model

This paper deals with the question of optimal debugging times for large-scale computer programs, given certain assumptions concerning the distribution of error within the program, the cost that is incurred whenever a fault occurs, and the cost of debugging. Modeling takes place within the framework of independent Poisson processes describing faults caused by errors. The optimization aim is to minimize the long-run total expected discounted cost. Some general results concerning the optimal polity are presented.This research has been supported by NATO Grant No. RG.86/0024, by the Alexander von Humboldt Foundation, and by the University of Passau, Germany.     

A generalized JM model with applications to imperfect debugging in software reliability

For their nice mathematical properties, state space models have been widely used, especially for forecasting. Over the last decades, the study of tracking software reliability by statistical models has attracted scientists’ attention. However, most of models focus on perfect debugging although practically imperfect debugging arises everywhere. In this paper, a non-Gaussian state space model is modified to predict software failure time with imperfect debugging. In fact, this model is very flexible so that we can modify the system equation in this model to satisfy the various situations. Besides, this model is suitable for tracking software reliability, and applied to two well known datasets on software failures.     

Statistical assertion: A more powerful method for debugging scientific applications

Traditional debuggers are of limited value for modern scientific codes that manipulate large complex data structures. Current parallel machines make this even more complicated, because the data structure may be distributed across processors, making it difficult to view/interpret and validate its contents. Therefore, many applications’ developers resort to placing validation code directly in the source program. This paper discusses a novel debug-time assertion, called a “Statistical Assertion”, that allows using extracted statistics instead of raw data to reason about large data structures, therefore help locating coding defects. In this paper, we present the design and implementation of an ‘extendable’ statistical-framework which executes the assertion in parallel by exploiting the underlying parallel system. We illustrate the debugging technique with a molecular dynamics simulation. The performance is evaluated on a 20,000 processor Cray XE6 to show that it is useful for real-time debugging.     

Finding duplicate rows in a linear programming model

We present an algorithm for identifying and disposing of duplicate rows in an LP matrix — that is, rows which are identical except for a scalar multiplier. This algorithm is a useful tool for debugging models as well as a means of improving efficiency. Implementation in a large scale mathematical programming system and computational experience are discussed.     

An interactive programming system for Pascal

Interactive program development tools are being increasingly recognized as helpful in the construction of programs. This paper describes an integrated incremental program development system for Pascal called Pathcal. Pathcal contains facilities for creation, editing, debugging and testing of procedures and programs. The system facilities are all Pascal procedures or variables and because of this allows the programmer to program the system in itself.This work was supported by The National Swedish Board of Technical Development (STU) under contract dnr. 78-4167.     

A Markov modulated Poisson model for software reliability

In this paper, we consider a latent Markov process governing the intensity rate of a Poisson process model for software failures. The latent process enables us to infer performance of the debugging operations over time and allows us to deal with the imperfect debugging scenario. We develop the Bayesian inference for the model and also introduce a method to infer the unknown dimension of the Markov process. We illustrate the implementation of our model and the Bayesian approach by using actual software failure data.     

A State Space Model for Software Reliability

In this paper, we propose a non-Gaussion state space model to apply in software reliability. This model assumes an exponential distribution for the failure time in every test-debugging stage, conditionally on the state parameter — the number of faults in the program. It is a generalized JM model which can be applied to the imperfect debugging situation as well as in evolving programs. By examining a set of data on evolving program failures, the effect of evolving program model is amply proved.     

An architecture for integrating logical and physical animations for general-purpose graphical simulation systems

Animation of the logical model is important for debugging the model, and animation of the physical system is important for communicating the results to others. To assure accuracy and consistency between these two animation views of the system, it is necessary to integrate the model building with the production of each animation. This paper describes a new architecture for developing simulation models so that the model created can be used to display both the animation of the system logic and the animation of the physical system.     

A framework for building spreadsheet based decision models

Spreadsheet based decision modelling is widely used in business today. The functionality provided within the current integrated packages allows users to develop very complex business models. Unfortunately, most spreadsheet users do not follow any particular methodology when building spreadsheet based models. This in turn leads to many wasted hours of debugging and re-programming and often produces models that look more complex than they need to be and are also tedious to extend or modify. This paper presents an example of the type of framework that can be used when developing spreadsheet based business models. The framework encourages the efficient development of more effective models that are easy to use and easy to modify.     

Imperfect debugging in software reliability: A Bayesian approach

The objective of studying software reliability is to assist software engineers in understanding more of the probabilistic nature of software failures during the debugging stages and to construct reliability models. In this paper, we consider modeling of a multiplicative failure rate whose components are evolving stochastically over testing stages and discuss its Bayesian estimation. In doing so, we focus on the modeling of parameters such as the fault detection rate per fault and the number of faults. We discuss how the proposed model can account for “imperfect debugging” under certain conditions. We use actual inter-failure data to carry out inference on model parameters via Markov chain Monte Carlo methods and present additional insights from Bayesian analysis.     

The ANALYZE rulebase for supporting LP analysis

This paper describes how to design rules to support linear programming analysis in three functional categories: postoptimal sensitivity, debugging, and model management. The ANALYZE system is used to illustrate the behavior of the rules with a variety of examples. Postoptimal sensitivity analysis answers not only the paradigmWhat if …? question, but also the more frequently askedWhy …? question. The latter is static, asking why some solution value is what it is, or why it is not something else. The former is dynamic, asking how the solution changes if some element is changed. Debugging can mean a variety of things; here the focus is on diagnosing an infeasible instance. Model management includes documentation, verification, and validation. Rules are illustrated to provide support in each of these related functions, including some that require reasoning about the linear program's structure. Another model management function is to conduct a periodic review, with one of the goals being to simplify the model, if possible. The last illustration is how to test new rule files, where there is a variety of ways to communicate a result to someone who is not expert in linear programming.     

Is There a Place in OR for Intelligent Tutoring Systems?

Intelligent tutoring systems (ITS) have recently found a wide range of potential applications across a multitude of disciplines and subject areas. This paper investigates the suitability and feasibility of developing ITS in the general context of OR as well as in a specific OR activity, simulation. It also examines how a current teaching aid for simulation model debugging can be transformed into an ITS and proposes a specification for the transformation process. The paper concludes that ITS could be useful in OR either for use as prime training systems or integrated in existing OR teaching packages. However, the field of ITS is in its infancy and development efforts are likely to be labour-intensive.     

A half year's experience with the facit-algol 1 compiler

The Facit-Algol 1 compiler is described briefly. Restrictions compared to Algol 60 and compatibility with other compilers is mentioned. The paper then discusses the experience with the compiler in the areas of teaching, programming, punching, debugging, and program exchanging. The conclusion is that Algol has done well and represents a great step forward.     

Enhancing software reliability modeling and prediction through the introduction of time-variable fault reduction factor

Over the past three decades, many software reliability models with different parameters, reflecting various testing characteristics, have been proposed for estimating the reliability growth of software products. We have noticed that one of the most important parameters controlling software reliability growth is the fault reduction factor (FRF) proposed by Musa. FRF is generally defined as the ratio of net fault reduction to failures experienced. During the software testing process, FRF could be influenced by many environmental factors, such as imperfect debugging, debugging time lag, etc. Thus, in this paper, we first analyze some real data to observe the trends of FRF, and consider FRF to be a time-variable function. We further study how to integrate time-variable FRF into software reliability growth modeling. Some experimental results show that the proposed models can improve the accuracy of software reliability estimation. Finally, sensitivity analyses of various optimal release times based on cost and reliability requirements are discussed. The analytic results indicate that adjusting the value of FRF may affect the release time as well as the development cost.     

An imperfect debugging model with two types of hazard rates for software reliability measurement and assessment

We propose a software reliability model which assumes that there are two types of software failures. The first type is caused by the faults latent in the system before the testing; the second type is caused by the faults regenerated randomly during the testing phase. The former and latter software failure-occurrence phenomena are described by a geometrically decreasing and a constant hazard rate, respectively. Further, this model describes the imperfect debugging environment in which the fault-correction activity corresponding to each software failure is not always performed perfectly. Defining a random variable representing the cumulative number of faults successfully corrected up to a specified time point, we use a Markov process to formulate this model. Several quantitative measures for software reliability assessment are derived from this model. Finally, numerical examples of software reliability analysis based on the actual testing data are presented.     

A note on estimating the number of errors in a system by recapture sampling

Some distributional results related to the recapture debugging experiment of Nayak (1988) for estimating the number of errors, N, in a software are obtained. It is proved that while the sufficient statistics are complete, an unbiased estimator of N based on them does not exist.     

A discrete time model for software reliability with application to a flight control software

This work is motivated by a particular software reliability problem in a unit of flight control software developed by the Indian Space Research Organization (ISRO), in which the testing of the software is carried out in multiple batches, each consisting of several runs. As the errors are found during the runs within a batch, they are noted, but not debugged immediately; they are debugged only at the end of that particular batch of runs. In this work, we introduce a discrete time model suitable for this type of periodic debugging schedule and describe maximum likelihood estimation for the model parameters. This model is used to estimate the reliability of the software. We also develop a method to determine the additional number of error‐free test runs required for the estimated reliability to achieve a specific target with some high probability. We analyze the test data on the flight control software of ISRO. Copyright © 2011 John Wiley & Sons, Ltd.