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One of the most important decisions related to the efficient management of testing phase of software development life cycle is to determine when to stop testing and release the software in the market. Most of the testing processes are imperfect once. In this paper first we have discussed an optimal release time problem for an imperfect faultdebugging model due to Kapur et al considering effect of perfect and imperfect debugging separately on the total expected software cost. Next, we proposed a SRGM incorporating the effect of imperfect fault debugging and error generation. The proposed model is validated on a data set cited in literature and a release time problem is formulated minimizing the expected cost subject to a minimum reliability level to be achieved by the release time using the proposed model. Solution method is discussed to solve such class of problem. A numerical illustration is given for both type of release problem and finally a sensitivity analysis is performed.

Testing phase of a software begins with module testing. During this period modules are tested independently to remove maximum possible number of faults within a specified time limit or testing resource budget. This gives rise to some interesting optimization problems, which are discussed in this paper. Two Optimization models are proposed for optimal allocation of testing resources among the modules of a Software. In the first model, we maximize the total fault removal, subject to budgetary Constraint. In the second model, additional constraint representing aspiration level for fault removals for each module of the software is added. These models are solved using dynamic programming technique. The methods have been illustrated through numerical examples.

ENGLISH ABSTRACT: Numerous Software Reliability Growth Models (SRGMs) have been discussed in the literature. These models are used to predict fault content and reliability of software. It has been observed that the relationship between testing time and the corresponding number of faults removed is either exponential or S-shaped, or a mix of the two. Another important class of SRGMs, known as flexible SRGMs, can depict both exponential and S-shaped growth curves. The paper introduces a new concept of power logistic learning function that proves to be very flexible, in the sense that it represents various curve types – exponential, Rayleigh, Weibull or simple logistic. The flexible nature of the power logistic function gives the flexible SRGM a higher degree of accuracy and wider applicability.AFRIKAANSE OPSOMMING: Verskeie voorbeelde van Betroubaarheidsgroeimodelle vir programmatuur word in die literatuur beskryf. Die modelle word gebruik vir die voorspelling van foutinhoud en programmatuurbetroubaarheid. Daar word waargeneem dat die verband tussen toetstyd en die resulterende foutverwydering eksponensiaal of S-vormig of ‘n kombinasie daarvan is. Aanpasbare modelle insluitende diskrete ekwivalente word ook behandel. Die publikasie ontleed vervolgens algemene plooibare maglogistieke leerkromme met wye toepasbaarheid wat slaan op eksponensiële, Rayleigh-, Weilbull- en logistieke funksies. Die plooibaarheid van die model waarborg akkuraatheid en wye toepasbaarheid met die verlangde gehalte van voorspelbaarheid.

ENGLISH ABSTRACTA Non-Homogenous Poisson Process (NHPP) model whose failure intensity function has the same mathematical form as that of a generalized exponential function was proposed for application as a Software Reliability Growth Model (SRGM). However, in order to facilitate collecting quality metrics pertaining to the degree of imperfect or efficient debugging phenomena and the number of faults left in the software, in this paper the authors propose an extension to the above SRGM. This SRGM enables adequate goodness of fit statistic and predictive validity, even when the software projects witness learning phenomenon of the testing team, either imperfect or perfect or efficient software debugging phenomenon, as well as wide fluctuations in time between failures – either occurring alone or in combinations thereof.AFRIKAANSE OPSOMMING: 'n Nie-homogene Poissonproses (NHPP) waarvan die mislukkingsdigtheidsfunksie soortgelyk is aan 'n algemene eksponensiële funksie word voorgehou as 'n programmatuur-betroubaarheidsgroeimodel (PBGM). Die model lewer toereikende passingsgoedheid en voorspellingsgeldigheid onder uiteenlopende leereienskappe van toetsers, swak of goeie ontfouting van programmatuur, en groot verskille tussen waardes van tyd tussen mislukkings. Die outeurs stel ook voor dat die goedheid van ontfoutingsaksies gemeet word met behulp van 'n uitbreiding van die PBGM-model.