The iterative and incremental development is becoming a major development process model in industry, and allows us for a good deal of parallelism between development and testing. In this paper we develop a quantitative software reliability assessment method in incremental development processes, based on the familiar non-homogeneous Poisson processes. More specifically, we utilize the software metrics observed in each incremental development and testing, and estimate the associated software reliability measures. In a numerical example with a real incremental developmental project data, it is shown that the estimate of software reliability with a specific model can take a realistic value, and that the reliability growth phenomenon can be observed even in the incremental development scheme.

Software system developed for a specific user under contract undergoes a period of testing by the user before acceptance. This is known as user acceptance testing and is useful to debug the software in the user's operational circumstance. In this paper we first present a simple non-homogeneous Poisson process (NHPP)-based software reliability model to assess the quantitative software reliability under the user acceptance test, where the idea of an accelerated life testing model is introduced to represent the user's operational phase and to investigate the impact of user's acceptance test. This idea is applied to the reliability assessment of web applications in a different testing environment, where two stress tests with normal and higher workload conditions are executed in parallel. Through numerical examples with real software fault data observed in actual user acceptance and stress tests, we show the applicability of the software accelerated life testing model to two different software testing schemes.

Failure phenomena of Web server systems are considered to depend on their workload characteristics. In this paper we focus on an Apache server system and analyze the real access/error logs. Based on parametric and non-parametric statistics, we characterize the web server failure from both theoretical and empirical points of view. As the result, it can be shown that the number of sessions strongly affects to the failure rate property of the Apache server.