Explorative study to provide decision support for software release decisions (original) (raw)
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Review on Software Reliability Growth Models and Software Release Planning
International Journal of Computer Applications, 2013
In this paper, Software Reliability Engineering is a field that developed from ancestry in the reliability disciplines of structural, electrical, and hardware engineering. Reliability models are powerful tools of Software Reliability Engineering for estimating, predicting, devious, and assessing software reliability. On the basis of the review the cataloging of software reliability models has been presented as a major part. This categorization is based on the various dimensions of reliability models. Models under review reflect either infinite or finite number of failures. This paper discusses a twodimensional software reliability growth modeling framework. We measured that an actual software reliability growth progression depends not only on testing time but also on testing effort and also enables us to portray software release planning problem in software reliability growth process. Thus, we can say that software project managers can demeanor more viable and accurate software reliability appraisal by using two-dimensional SRGM.
Consequences of Mispredictions of Software Reliability: A Model and its Industrial Evaluation
2014 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement, 2014
Predicting reliability of software under development is an important part of estimations in software engineering projects. In many organizations as the goal is that software products are released with no known defects, the process of finding and removing defects correlates with the effort for software projects. Software development projects estimate the resources needed to design, develop, test and release software products, and the number of defects which have to be handled. In this paper we present a model for consequence analysis of inaccurate predictions of quality in software projects. The model is a result of multiple case studies and is evaluated at two companies. The model recognizes the most common mispredictionse.g. over-and under-prediction, early-and latepredictionsand the combination of theses. The results from the industrial evaluation show that the consequences can be grouped according to under-and over-predictions and that the late-and early-predictions have the same consequences. The results show also that mispredicting the shape of the reliability curve has a significant consequence with regard to assessment of release readiness and resource planning.
Two Practical Software Reliability Growth Models for Software Project Management
ABSTRACT Software project management is highly competitive and challenging than ever before, due to enlightened customers and quick obsolescence of technology and the products have to be delivered on time, within budget and with lesser number of defects. Every project is also unique and in order to be successful, lessons learnt in one project are to be used quickly in every ongoing and future project. Software reliability engineering which has remained an active area of research over the past 40 years, throws a number of metrics which can be used effectively to steer current and future projects to the satisfaction of all the stakeholders. Some vital metrics which can be accurately brought out by Software Reliability Growth Models include total number of faults present at completion of software integration, the learning index of the testing team, quality of debugging, software release time, patent faults etc. In this paper, 2 Generalized Non-Homogenous Poisson Process (NHPP) Software Reliability Growth Models (SRGM) are presented which are found to estimate the software release time reasonably accurately and consistently. In addition, the SRGMs also provide the vital metrics discussed above readily, which may be found to be immensely useful for efficient software project management.
Experimenting Traditional and Modern Reliability Models in a 3-Years European Software Project
Reliability is a very important non-functional aspect for software systems and artefacts. In literature, several definitions of software reliability exist and several methods and approaches exist to measure reliability of a software project. However, in the literature no works focus on the applicability of these methods in all the development phases of real software projects. In this paper, we describe the methodology we adopted during the S-CASE FP7 European Project to predict reliability for both the S-CASE platform as well as for the software artefacts automatically generated by using the S-CASE platform. Two approaches have been adopted to compute reliability: the first one is the ROME Lab Model, a well adopted traditional approach in industry; the second one is an empirical approach defined by the authors in a previous work. An extensive dataset of results has been collected during all the phases of the project. The two approaches can complement each other, to support to prediction of reliability during all the development phases of a software system in order to facilitate the project management from a non-functional point-of-view.
What is Hampering the Performance of Software Reliability Models? A literature review
2009
This article explores the critical factors and issues that impede the performance of software reliability modeling science. The literature review indicates that software reliability models have not delivered the desirable deliverables that they are intended to realize. The current work suggests that the reasons for such performance incompetence of the software reliability modeling are attributed to eight major causes. Based upon the findings of the current study, a simple framework is proposed to provide guidelines to developing software organizations in order to improve the performance of software reliability modeling concept.
Journal of Systems and Software, 2014
During software development two important decisions organizations have to make are: how to allocate testing resources optimally and when the software is ready for release. SRGMs (Software Reliability Growth Models) provide empirical basis for evaluating and predicting reliability of software systems. When using SRGMs for the purpose of optimizing testing resource allocation, the model's ability to accurately predict the expected defect inflow profile is useful. For assessing release readiness, the asymptote accuracy is the most important attribute. Although more than hundred models for software reliability have been proposed and evaluated over time, there exists no clear guide on which models should be used for a given software development process or for a given industrial domain.
Predicting Time Range of Development Based on Generalized Software Reliability Model
2014 21st Asia-Pacific Software Engineering Conference, 2014
Development environments have changed drastically, development periods are shorter than ever and the number of team members has increased. These changes have led to difficulties in controlling the development activities and predicting when the development will end. Especially, quality managers try to control software reliability and project managers try to estimate the end of development for planing developing term and distribute the manpower to other developments. In order to assess recent software developments, we propose a generalized software reliability model (GSRM) based on a stochastic process, and simulate developments that include uncertainties and dynamics. We also compare our simulation results to those of other software reliability models. Using the values of uncertainties and dynamics obtained from GSRM, we can evaluate the developments in a quantitative manner. Additionally, we use equations to define the uncertainty regarding the time required to complete a development, and predict whether or not a development will be completed on time. We compare GSRM with an existing model using two old actual datasets and one new actual dataset which we collected, and show that the approximation curve generated by GSRM is about 12% more precise than that generated by the existing model. Furthermore, GSRM can narrow down the predicted time range in which a development will end to less than 40% of that obtained by the existing model.
Categorization of Software Release Risks and Its Abatement Strategy
Journal of Software Engineering and Applications, 2014
Growing competition in the software industry with the persistently changing needs and the usual problems associated with software release, which have made acceptance of a new software in market, are extremely important for the success. Volatility in the software developmental processes is generally difficult to handle. The change request at any arbitrary point of time leads to the inevitable change and rework request. The software release process which broadly includes all the process that starts after the completion of development till the final deployment. This complete phase is exposed to various risks which may hamper the final result. This paper presents threat associated with software release activities and their possible mitigation and exploring the role played by the change management in controlling or reducing those risks.For the effective survival in ever changing software industry needs, Software Release Management takes a holistic view of the change and configuration relationship and work on the improvement strategies for the effective release with zero defect potential.