A testability-dependent maintainability-prediction technique (original) (raw)

Maintainability Derivations Using the Analytical Maintenance Model

IEEE Transactions on Reliability, 2000

The application of the analytical maintenance model worthy of note by way of initroducinig the gamma. distrlibuto the analysis of the launch readiness of systems is described, and tion as a useful descriptor of downtime frequencies of the maintainability contribution to the system operational readiness is also derived. Where the probability of multiple malfunctions loccurence. the dsrbtion mostcm ly sed gar the during a time-limited operational sequence is significant, the speci-log normal and the negative exponential. The gamma disfication of maintenance capability can become involved. A simplifying tribution should be conisidered as a potenitial alternate to process using the gamma distribution is described which expresses the commronly used distributions due to its relative algethe maintenance capability in terms of a probability of performing braic simplicity. The rationiale for this approach is dethe restore function within a time limitation. Similarities between scribed. The potenitial usefulniess of the procedures dethe empirical distribution and the gamma distribution are dis-.. ap .. cussed. These similarities are worthy of note by way of introducing veloped will apply to maintainablity prediction, assessthe gamma distribution as a useful descriptor of downtime fre-menit, ancd validation, anid further to system or subsystem quencies of occurrence. The distributions most commonly used are levels. the log normal and the negative exponential. The reason for considering the gamma distribution as a potential alternate is its GENERAL relative algebraic simplicity. The rationale for this approach is. described. The potential usefulness of the procedures developed will no t o'get cplet, statistial sound timtso apply to maintainability prediction, assessment, and validation, of the maintenance capablity to restore combinations of and further to system or subsystem levels. malfunctions, as well as single malfunctions, a total of 121 computer runs were made. It was found that 72 com

Maintainability prediction: a regression analysis of measures of evolving systems

21st IEEE International Conference on Software Maintenance (ICSM'05), 2005

In order to build predictors of the maintainability of evolving software, we first need a means for measuring maintainability as well as a training set of software modules for which the actual maintainability is known. This paper describes our success at building such a predictor. Numerous candidate measures for maintainability were examined, including a new compound measure. Two datasets were evaluated and used to build a maintainability predictor. The resulting model, Maintainability Prediction Model (MainPredMo), was validated against three held-out datasets. We found that the model possesses predictive accuracy of 83% (accurately predicts the maintainability of 83% of the modules). A variant of MainPredMo, also with accuracy of 83%, is offered for interested researchers.

United States Patent ( 19 ) McEnroe et al . ( 54 AUTOMATED SYSTEM TESTABILITY ASSESSMENT METHOD

2017

A procedure for calculating maintainability and testabil ity parameters of a complex system uses computer soft ware to enable the calculations to be made repeatedly during the development of the system. Failure modes and failure rates, elemental task times and test path data from a branching test flow diagram are input. Screens which identify the data to be input are displayed for ease of data entry. A hierarchical relationship between the modules in the system can be entered so that failure modes and failure rates need only be entered for lowest level modules. The procedure iteratively calculates maintainability and testability parameters starting at the lowest level and using previously calculated data in the next highest level. Fault isolation ambiguity is automati cally taken into account by ordering the modules in descending order of total test path/module failure rate isolated by each test path. The ordered data are used in many of the calculations of the maintainability and t...

Predicting Faults before Testing Phase using Halstead’s Metrics

Software designers are motivated to utilize off-the-shelf software components for rapid application development. Such applications are expected to have high reliability as a result of deploying trusted components. This paper introduces Halstead’s software science to predict the fault before testing phase for component based system. Halstead’s software science is used to predict the faults for individual component and based on this faults reliability of different component is measured so that only reliable component will be reused.

Methodology for maintainability-based risk assessment

2006

Abstract A software product spends more than 65% of its lifecycle in maintenance. Software systems with good maintainability can be easily modified to fix faults or to adapt to changing environment. We define maintainability-based risk as a product of two factors: the probability of performing maintenance tasks and the impact of performing these tasks. In this paper, we present a methodology for assessing maintainability-based risk to account for changes in the system requirements.

Testability Estimation Framework

International Journal of Computer Applications, 2010

Testability has always been an elusive concept and its correct measurement or evaluation a difficult exercise. Most of the studies measure testability or more precisely the attributes that have impact on testability but at the source code level. Though, testability measurement at the source code level is a good indicator of effort estimation, it leads to the late arrival of information in the development process. A decision to change the design in order to improve testability after coding has started may be very expensive and error-prone. While estimating testability early in the development process may greatly reduce the overall cost. This paper provides a roadmap to industry personnel and researchers to assess, and preferably, quantify software testability in design phase. A prescriptive framework has been proposed in order to integrate testability within the development life cycle. It may be used to benchmark software products according to their testability.

A Brief Review of Software Reliability Prediction Models

Software plays an important role in every field of human activity today varying from medical diagnosis to remote controlling spacecraft. Hence it is important for the software to provide failure-free performance whenever needed. The Information technology industry has witnessed rapid growth in the recent past. The competition among the firms also increased. The software organization in the developing countries like India can no longer survive on cost advantage alone. The software companies need to deliver reliable and quality software on time. A lot of research has been carried out on software quality management and reliability estimation. The objective of this paper is to provide a brief review of the major research contribution in the field of software reliability and identify the future research areas in software reliability estimation and prediction Keywords: software reliability growth models, nonhomogeneous Poisson process models, s-shaped models, imperfect debugging I. INTRODUCTION Many organizations utilize information technology (IT) to improve productivity, enhance operational efficiency, responsiveness, etc [1] As a result, the IT industry has witnessed tremendous growth in the past few decades. As the number of information technology companies increased, the competition among them also increased. The software organization in the developing countries like India can no longer survive or grow based on cost advantage alone. But delivering reliable and quality software on time within budgeted cost is a challenge for many organizations [2], [3]. Many times the companies would compromise on software testing and release the software with residual defects. This would make the software unreliable. The software reliability is defined as the probability of failure-free operation of a software system for a specified time in a specified environment [4]. The failure of the software during operations can lead to customer dissatisfaction, loss of market share, etc. The failure of a software used in the medical device or that used in air traffic control system can have a disastrous effect on the individual as well as society. Hence it is imperative for the software firms to ensure their product is sufficiently reliable before releasing the software for usage. This paper is a brief review of the important developments happened in the field of software reliability and identifies the future research areas. The remaining part of this article is arranged as follows: the session II describes the literature review methodology, the literature review analysis is given in session III and the conclusion are discussed in session IV. II. LITERATURE REVIEW METHODOLOGY A lot of articles have been presented at conferences, published in journals and books have been written in the last few decades on software reliability estimation and prediction. The aim of this paper is to provide a brief review of the important researches carried on developing software reliability models. The process started with searching for relevant published articles. The scope of the review is limited to the published books and papers published in journals and important conference proceedings. The databases searched are IEEE explore, Science direct, Google scholar and research gate. Two hundred and nine papers are identified for review. After reading the abstract, ninety-seven papers are shortlisted for review. Another twenty-nine papers are later dropped as the content is not directly related to the focus area of the review. Finally, sixty-eight papers are included in the review. The details are given in fig 1.

Advanced models for software reliability prediction

2011 Proceedings - Annual Reliability and Maintainability Symposium, 2011

This article describes the advanced parametric models for assessment and prediction of software reliability, based on statistics of bugs at the initial stage of testing. The parametric model approach, commonly associated with reliability issues, deals with the evaluation of the amount of bugs in the code. Computed parameter values inserted into the model allow to estimate: (a) number of bugs remaining in the product, and (b) time required to detect the remaining bugs. Many models are developed for similar purpose: Duane Reliability Growth Model, Goel Model, Weibull Model, Classical S-shaped Model, Ohba S-shaped Model, etc. Taking into account some detailed, but practical, aspects of the software testing process, a few Advanced Models were developed and usefully implemented by the authors. The proposed models are sensitive to the situations typical for the early stages of Software development. As a result, one deals with the essentially non-linear, multimodal goal function to define the optimal value as the estimation of the unknown control parameter. To support the optimization of such complex models, the Cross-Entropy Global Optimization Method is proposed. Some authentic numerical examples are considered to demonstrate the efficiency of the proposed models.

Needs and Importance of Reliability Prediction: An Industrial Perspective

Information Sciences Letters, 2020

Reliability plays a really important role for getting quality software. Already developed models of reliability prediction are well-recognised resources to support the management of software quality. Though many practitioners proposed several reliability prediction models but still, these prediction models have various problems during the use in the industry because there is a gap between proper implementation of software reliability prediction model development and their industrial use. There is a need to fill the gap between these two bridges of problems. Also the literature review of previous work and best practices, discloses the noticeable needs and importance of software reliability prediction. With this, author also gives some suggestions to practitioners for increasing the usability of the reliability prediction models. This article may help to developers for reducing the failure rate and enhancing the software reliability.