No abstract available.

Software inspection is one of the most effective methods to detect defects. Reinspection repeats the inspection process for software products that are suspected to contain a significant number of undetected defects after an initial inspection. As a reinspection is often believed to be less efficient than an inspection an important question is whether a reinspection justifies its cost. In this paper we propose a cost-benefit model for inspection and reinspection. We discuss the impact of cost and benefit parameters on the net gain of a reinspection with empirical data from an experiment in which 31 student teams inspected and reinspected a requirements document. Main findings of the experiment are: a) For reinspection benefits and net gain were significantly lower than for the initial inspection. Yet, the reinspection yielded a positive net gain for most teams with conservative cost-benefit assumptions. B) Both the estimated benefits and number of major defects are key factors for reinspection net gain, which emphasizes the need for appropriate estimation techniques.

Capture-recapture (CR) models have been proposed as an objective method for controlling software inspections. CR models were originally developed to estimate the size of animal populations. In software, they have been used to estimate the number of defects in an inspected artifact. This estimate can be another source of information for deciding whether the artifact requires a reinspection to ensure that a minimal inspection effectiveness level has been attained. Little evaluative research has been performed thus far on the utility of CR models for inspections with two inspectors. We report on an extensive Monte Carlo simulation that evaluated capture-recapture models suitable for two inspectors assuming a code inspections context. We evaluate the relative error of the CR estimates as well as the accuracy of the reinspection decision made using the CR model. Our results indicate that the most appropriate capture-recapture model for two inspectors is an estimator that allows for inspectors with different capabilities. This model always produces an estimate (i.e., does not fail), has a predictable behavior (i.e., works well when its assumptions are met), will have a relatively high decision accuracy, and will perform better than the default decision of no reinspections. Furthermore, we identify the conditions under which this estimator will perform best.

The basic premise of software inspections is that they detect and remove defects before they propagate to subsequent development phases where their detection and correction cost escalates. To exploit their full potential, software inspections must call for a close and strict examination of the inspected artifact. For this, reading techniques for defect detection may be helpful since these techniques tell inspection participants what to look for and, more importantly, how to scrutinize a software artifact in a systematic manner. Recent research efforts investigated the benefits of scenario-based reading techniques. A major finding has been that these techniques help inspection teams find more defects than existing state-of-the-practice approaches, such as, ad-hoc or checklist-based reading (CBR). We experimentally compare one scenario-based reading technique, namely, perspective-based reading (PBR), for defect detection in code documents with the more traditional CBR approach. The comparison was performed in a series of three studies, as a quasi experiment and two internal replications, with a total of 60 professional software developers at Bosch Telecom GmbH. Meta-analytic techniques were applied to analyze the data.

No abstract available.

An important requirement to control the inspection of software artifacts is to be able to decide, based on more objective information, whether the inspection can stop or whether it should continue to achieve a suitable level of artifact quality. A prediction of the number of remaining defects in an inspected artifact can be used for decision making. Several studies in software engineering have considered capture-recapture models to make a prediction. However, few studies compare the actual number of remaining defects to the one predicted by a capture-recapture model on real software engineering artifacts. The authors focus on traditional inspections and estimate, based on actual inspections data, the degree of accuracy of relevant state-of-the-art capture-recapture models for which statistical estimators exist. In order to assess their robustness, we look at the impact of the number of inspectors and the number of actual defects on the estimators' accuracy based on actual inspection data. Our results show that models are strongly affected by the number of inspectors, and therefore one must consider this factor before using capture-recapture models. When the number of inspectors is too small, no model is sufficiently accurate and underestimation may be substantial. In addition, some models perform better than others in a large number of conditions and plausible reasons are discussed. Based on our analyses, we recommend using a model taking into account that defects have different probabilities of being detected and the corresponding Jackknife Estimator. Furthermore, we calibrate the prediction models based on their relative error, as previously computed on other inspections. We identified theoretical limitations to this approach which were then confirmed by the data.

The value of software inspection for uncovering defects early in the development lifecycle has been well documented.Of the various types of inspection methods published to date, experiments have shown perspective-based inspection to be one of the most effective, because of its enhanced coverage of the defect space.However, inspections in general, and perspective-based inspections in particular, have so far been applied predominantly in the context of conventional structured development methods, and then almost always to textual artifacts, such as requirements documents or code modules.Object oriented-models, particularly of the graphical form, have so far not been adequately addressed by inspection methods.This paper tackles this problem by first discussing the difficulties involved in tailoring the perspective-based inspection approach to object-oriented development methods and, second, by presenting a generalization of the approach which overcomes these limitations.The new version of the approach is illustrated in the context of UML-based object-oriented development.

It is difficult for organizations introducing and using software inspections to evaluate how efficient they are. However, it is of practical importance to determine whether they have been efficiently implemented or whether further corrective actions are necessary to bring them up to standard. We present in this paper a procedure for building inspection efficiency benchmarks based on simulation and typical inspection data. Based on most of the data published in the literature, we build an industry-wide benchmark which intends to capture the current practice regarding inspection efficiency. This benchmark construction procedure can also be used to build enterprise specific benchmarks. Last, we assess how robust we can expect them to be by distorting their input distributions to reflect violations of the assumptions made.

The paper contributes a controlled experiment to characterize the effects of code inspection and structural testing on software quality. Twenty subjects performed sequentially code inspection and structural testing using different coverage values as test criteria on a C-code module. The results of this experiment show that inspection significantly outperforms the defect detection effectiveness of structural testing. Furthermore, the experimental results indicate little evidence to support the hypothesis that structural testing detects different defects, that is, defects of a particular class, that were missed by inspection and vice versa. These findings suggest that inspection and structural testing do not complement each other well. Since 39 percent (on average) of the defects were not detected at all, it might be more valuable to apply inspection, together with other testing techniques, such as boundary value analysis, to achieve a better defect coverage. We are aware that a single experiment has many limitations and often does not provide conclusive evidence. Hence, we consider this experiment a starting point and encourage other researchers to investigate the optimal mix of defect detection techniques.

An important requirement to control the inspection of software artifacts is to be able to decide, based on objective information, whether inspection can stop or whether it should continue to achieve a suitable level of artifact quality. Several studies in software engineering have considered the use of capture-recapture models to predict the number of remaining defects in an inspected document as a decision criterion about reinspection. However, no study on software engineering artifacts compares the actual number of remaining defects to the one predicted by a capture-recapture model. Simulations have been performed but no definite conclusions can be drawn regarding the degree of accuracy of such models under realistic inspection conditions, and the factors affecting this accuracy. Furthermore, none of these studies performed an exhaustive comparison of existing models. In this study, we focus on traditional inspections and estimate, based on actual inspection data, the degree of accuracy of all relevant, state-of-the-art, capture-recapture models for which statistical estimators exist. We compare the various models' accuracies and look at the impact of the number of inspectors on these accuracies. Results show that model accuracies are strongly affected by the number of inspectors and, therefore, one must consider this factor before using capture-recapture models. When the number of inspectors is below 4, no model is sufficiently accurate and underestimation may be substantial. In addition, some models perform better than others in a large number of conditions and plausible reasons are discussed. Based on our analyses, we recommend using a model taking into account different probabilities of detecting defects and a Jacknife estimator.