Search-based Test Case Generation (TCG) for web applications suffers from unstable performance and suboptimal test suite problems due to diversity loss. However, previous diversity metrics mainly only focus on client-side models or server-side code, which are prone to low robustness and poor generalization in practical applications. We propose a diversity-driven TCG method DTester, which can maximize behavior exploration and minimize the test suite size while covering more server-side vulnerable paths. Three diversity metrics (i.e. phenotypic coupling, intent coupling and competitiveness) are proposed to measure the underlying relationship between test cases from user behavior, code logic and test execution history. Moreover, a 3-dimensional weight graph is designed to model association among metrics, which provides fine-grained guidance for the genetic algorithm to generate diverse test cases from the client-side behavior model. Our empirical evaluation on five web applications shows that DTester can efficiently and robustly generate better test suites than the state-of-the-art TCG method. The maximum improvement is [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] in efficiency, test suite size, diversity and robustness.

Automatically localizing faulty statements is a desired feature for effective learning programming. Most of the existing automated fault localization techniques are developed and evaluated on commercial or well-known open-source projects, which performed poorly on novice programs. In this paper, we propose a novel fault localization technique VsusFL (Variable-suspiciousness-based Fault Localization) for novice programs. VsusFL is inspired by simulating the manual program debugging process and takes advantage of variable value sequences. VsusFL can trace variable value changes, determine whether the intermediate state of the variables is correct, and report the potential faulty statements for novice programs. This paper presents the implementation of VsusFL and conducts empirical studies on 422 real faulty novice programs. Experimental results show that VsusFL performs much better than Grace, ANGELINA, VSBFL, Spectrum-Based Fault Localization (SBFL), and Variable-based Fault Localization (VFL) in terms of T O P -1, T O P -3, and T O P -5 metrics. Specifically, VsusFL can localize 90%, 35% and 9% more faulty statements than the best-performing baseline Grace. Moreover, We analyze the correlation between VsusFL and other techniques and find a weak correlation since they perform well on different programs, indicating the potential to further enhance fault localization performance through strategic integration of VsusFL with other methods.