Software reliability modeling techniques have been touted as way of measuring and tracking software systems reliability. However a number of issues make it difficult to use and apply these models in practice. In this paper we show some of the challenges and issues that we have encountered in applying these techniques to track and predict a networking software system reliability behavior at two different stages of its life cycle. Through our case study we show some of the practical solutions we have adopted to overcome these challenges. We also try to establish a relationship between the software testing phase based reliability prediction and field software reliability measurement in order to derive a systematic tracking approach.

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A growing body of literature suggests that there is an optimal size for software components. This means that components that are too small or too big will have a higher defect content (i.e., there is a U-shaped curve relating defect content to size). The U-shaped curve has become known as the "Goldilocks Conjecture." Recently, a cognitive theory has been proposed to explain this phenomenon and it has been expanded to characterize object-oriented software. This conjecture has wide implications for software engineering practice. It suggests 1) that designers should deliberately strive to design classes that are of the optimal size, 2) that program decomposition is harmful, and 3) that there exists a maximum (threshold) class size that should not be exceeded to ensure fewer faults in the software. The purpose of the current paper is to evaluate this conjecture for object-oriented systems. We first demonstrate that the claims of an optimal component/class size (1) above) and of smaller components/classes having a greater defect content (2) above) are due to a mathematical artifact in the analyses performed previously. We then empirically test the threshold effect claims of this conjecture (3) above). To our knowledge, the empirical test of size threshold effects for object-oriented systems has not been performed thus far. We performed an initial study with an industrial C++ system and repeated it twice on another C++ system and on a commercial Java application. Our results provide unambiguous evidence that there is no threshold effect of class size. We obtained the same result for three systems using four different size measures. These findings suggest that there is a simple continuous relationship between class size and faults, and that, optimal class size, smaller classes are better and threshold effects conjectures have no sound theoretical nor empirical basis.

Much effort has been devoted to the development and empirical validation of object-oriented metrics. The empirical validations performed thus far would suggest that a core set of validated metrics is close to being identified. However, none of these studies allow for the potentially confounding effect of class size. We demonstrate a strong size confounding effect and question the results of previous object-oriented metrics validation studies. We first investigated whether there is a confounding effect of class size in validation studies of object-oriented metrics and show that, based on previous work, there is reason to believe that such an effect exists. We then describe a detailed empirical methodology for identifying those effects. Finally, we perform a study on a large C++ telecommunications framework to examine if size is really a confounder. This study considered the Chidamber and Kemerer metrics and a subset of the Lorenz and Kidd metrics. The dependent variable was the incidence of a fault attributable to a field failure (fault-proneness of a class). Our findings indicate that, before controlling for size, the results are very similar to previous studies. The metrics that are expected to be validated are indeed associated with fault-proneness.

A practical application of object oriented measures is to predict which classes are likely to contain a fault. This is contended to be meaningful because object oriented measures are believed to be indicators of psychological complexity, and classes that are more complex are likely to be faulty. Recently, a cognitive theory was proposed suggesting that there are threshold effects for many object oriented measures. This means that object oriented classes are easy to understand as long as their complexity is below a threshold. Above that threshold their understandability decreases rapidly, leading to an increased probability of a fault. This occurs, according to the theory, due to an overflow of short-term human memory. If this theory is confirmed, then it would provide a mechanism that would explain the introduction of faults into object oriented systems, and would also provide some practical guidance on how to design object oriented programs. The authors empirically test this theory on two C++ telecommunications systems. They test for threshold effects in a subset of the Chidamber and Kemerer (CK) suite of measures (S. Chidamber and C. Kemerer, 1994). The dependent variable was the incidence of faults that lead to field failures. The results indicate that there are no threshold effects for any of the measures studied. This means that there is no value for the studied CK measures where the fault-proneness changes from being steady to rapidly increasing. The results are consistent across the two systems. Therefore, we can provide no support to the posited cognitive theory.

Article Polymorphism measures for early risk prediction Share on Authors: Saïda Benlarbi Cistel Technology, 210 Colonnade Road, Suite 204, Nepean, Ontario, Canada K2E 7L5 Cistel Technology, 210 Colonnade Road, Suite 204, Nepean, Ontario, Canada K2E 7L5View Profile , Walcelio L. Melo Oracle Brazil and Univ. Católica de Brasilia, SQN Qd. 02- Bl A-Salas 604, Brasilia, DF Brazil 70712-900 Oracle Brazil and Univ. Católica de Brasilia, SQN Qd. 02- Bl A-Salas 604, Brasilia, DF Brazil 70712-900View Profile Authors Info & Claims ICSE '99: Proceedings of the 21st international conference on Software engineeringMay 1999 Pages 334–344https://doi.org/10.1145/302405.302652Online:16 May 1999Publication History 35citation573DownloadsMetricsTotal Citations35Total Downloads573Last 12 Months10Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access