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Software reliability is one of the standard critical inherent characteristics of software systems. The testing coverage function (TCF) is a significant parameter for identifying the completeness and effectiveness of software testing. It is defined as the proportion of the code that has been tested up to time t. To capture the dynamic behavior of the number of faults detected over a period of time, several distributions, namely S‐shaped, inflection S‐shaped, logistic, log‐logistic, Weibull, Rayleigh, Erlang, and logarithmic exponentiated, have been used as TCF in literature. However, these distributions are not sufficient to describe TCF's practical behavior due to complexity and vagueness in the collected data. This study proposes two software reliability growth models (SRGMs), which incorporate the generalized inflection S‐shaped (GISS) distribution as TCF. The models have been developed in perfect and imperfect debugging environments while considering fault removal efficiency, error generation, and uncertainty in the operating environment. To analyze the effectiveness, the proposed models are then tested with six failure data sets. The choice of GISS distribution as a TCF improves the software reliability estimation in comparison with the existing models in the literature. Finally, single and multiple parameters sensitivity analysis also has been done and based on it, the critical parameters have been detected. The proposed models may be helpful for the system analyst to predict various parameters about some software systems. [ABSTRACT FROM AUTHOR]

Effort‐Aware Defect Prediction (EADP) methods sort software modules based on the defect density and guide the testing team to inspect the modules with high defect density first. Previous studies indicated that some feature selection methods could improve the performance of Classification‐Based Defect Prediction (CBDP) models, and the Correlation‐based feature subset selection method with the Best First strategy (CorBF) performed the best. However, the practical benefits of feature selection methods on EADP performance are still unknown, and blindly employing the best‐performing CorBF method in CBDP to pre‐process the defect datasets may not improve the performance of EADP models but possibly result in performance degradation. To assess the impact of the feature selection techniques on EADP, a total of 24 feature selection methods with 10 classifiers embedded in a state‐of‐the‐art EADP model (CBS+) on the 41 PROMISE defect datasets were examined. We employ six evaluation metrics to assess the performance of EADP models comprehensively. The results show that (1) The impact of the feature selection methods varies in classifiers and datasets. (2) The four wrapper‐based feature subset selection methods with forwards search, that is, AdaBoost with Forwards Search, Deep Forest with Forwards Search, Random Forest with Forwards Search, and XGBoost with Forwards Search (XGBF) are better than other methods across the studied classifiers and the used datasets. And XGBF with XGBoost as the embedded classifier in CBS+ performs the best on the datasets. (3) The best‐performing CorBF method in CBDP does not perform well on the EADP task. (4) The selected features vary with different feature selection methods and different datasets, and the features noc (number of children), ic (inheritance coupling), cbo (coupling between object classes), and cbm (coupling between methods) are frequently selected by the four wrapper‐based feature subset selection methods with forwards search. (5) Using AdaBoost, deep forest, random forest, and XGBoost as the base classifiers embedded in CBS+ can achieve the best performance. In summary, we recommend the software testing team should employ XGBF with XGBoost as the embedded classifier in CBS+ to enhance the EADP performance. [ABSTRACT FROM AUTHOR]

To enter the production stage, in DevOps practices candidate software releases have to pass quality gates, where they are assessed to meet established target values for key indicators of interest. We believe software reliability should be an important such indicator, as it greatly contributes to the end‐user satisfaction. We propose DevOpRET, an approach for reliability testing as part of the acceptance testing stage in DevOps. DevOpRET relies on operational‐profile–based testing, a common reliability assessment technique. DevOpRET leverages usage and failure data monitored in operations to continuously refine its estimate. We evaluate accuracy and efficiency of DevOpRET through controlled experiments with a real‐world open source platform and with a microservice architectures benchmark. The results show that DevOpRET provides accurate and efficient estimates of the true reliability over subsequent DevOps cycles. [ABSTRACT FROM AUTHOR]

Various Software Reliability Growth Models (SRGMs) have been developed over last five decades to estimate the failure rate, software reliability and remaining number of faults in the software during testing phase of software development life cycle. There are numerous SRGMs available in the research literature and in the presence of numerous SRGM, appropriate selection is a staggering task for the software testing professionals. In the software reliability area, researchers have always been interested to select the optimal SRGM to use in a particular situation. In this study, we have developed an integrated technique based on Criteria Importance Through Inter Criteria Correlation (CRITIC) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to select the optimal SRGM. The weights of criteria are obtained by the CRITIC method and SRGMs are ranked by order using TOPSIS method. The working of proposed integrated CRITIC‐TOPSIS approach is illustrated on the two real‐time failure data sets. Furthermore, the methodology validation idea supports the current study by comparing the results obtained with the previous multi‐criteria decision making (MCDM) methodologies namely Additive Ratio Assessment (ARAS). The study revealed that the ranking of SRGMs can be solved effectively using the integration of CRITIC and TOPSIS. The result of this study plays a significant role to take decisions and to evaluate the feasibility of SRGM. [ABSTRACT FROM AUTHOR]

Software reliability literature consists of various change-point-based software reliability growth models and related release time problems. The primary assumption of the existing models is the existence of change-point before software release time only. This does not look practical as the testing team becomes more proficient in detecting the faults due to their continuous involvement in software development by the software release time. Hence the fault detection rate in the pre- and post release phase is not the same. To capture this change in fault detection rate in the pre- and post release testing phase, we propose a new software reliability modeling framework by considering two change-points during the software lifecycle; that is, there exists a change-point before release time and release time as a change-point. Further, in the last one-decade software firms have changed their strategy of stop testing the software after release and continue to test even after release to remove the number of faults to provide better user experiences. This phenomenon attracted academicians to develop theoretical as well empirical study on post release testing and formulation of related release time problem. In this paper, we propose a software cost model to determine optimal release and testing stop time considering under the assumption of two change-points as mentioned above. The proposed model is validated on real-life data set. [ABSTRACT FROM AUTHOR]

Owing to release of software in multiple releases, code changes take place in software. Because of this added complexity in software, the testing team may be unable to correct the fault upon detection, leaving the actual fault to reside in the software, termed as imperfect debugging or there may be replacement of original fault by other fault, leading to error generation. Many other factors exist that affect the testing phase of software like strategies of testing, test cases, skill, efficiency, and learning of testing team. All these factors cannot be kept stable during the whole process of testing. They may change at any time moment causing the background processes to experience change, which is known as change‐point. Keeping all these critical testing environment factors under consideration, a new software reliability growth model has been proposed, which is derived from an non homogenous Poisson process (NHPP)based unified scheme for multi‐release two‐stage fault detection/observation and correction/removal software reliability models. The developed model is numerically illustrated on tandem data set for four releases. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of determining the optimal testing period and the optimal maintenance limit for software. Software debugging in the testing phase and the operation stage is described by a software reliability model based on a nonhomogeneous Poisson process and models the difference between the software execution environments in both stages as the operational profile. Furthermore, the time when continued maintenance by the project team ends is defined as the planned maintenance limit. Each of the optimal testing period (release time) and the optimal planned maintenance limit that minimize the total expected software cost is determined. Finally, we present an algorithm that determines the joint optimal policies for both, and perform the optimal testing/maintenance design through numerical examples. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 89(8): 1–9, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20224 [ABSTRACT FROM AUTHOR]