Enterprise applications are difficult to test because their intended functionality is either not described precisely enough or described in cumbersome business rules. It takes a lot of effort on the part of a test architect to understand all the business rules and design tests that "cover" them, i.e., exercise all their constituent scenarios. Part of the problem is that it takes a complicated set up sequence to drive an application to a state in which a business rule can even fire. In this paper, we present a business rule modeling language that can be used to capture functional specification of an enterprise system. The language makes it possible to build tool support for rule authoring, so that obvious deficiencies in rules can be detected mechanically. Most importantly, we show how to mechanically generate test sequences--i.e., test steps and test data--needed to exercise these business rules. To this end, we translate the rules into logical formulae and use constraint solving to generate test sequences. One of our contributions is to overcome scalability issues in this process, and we do this by using a novel algorithm for organizing search through the space of candidate sequences to discover covering sequences. Our results on three case studies show the promise of our approach.

JavaScript programs often suffer from memory issues that can either hurt performance or eventually cause memory exhaustion. While existing snapshot-based profiling tools can be helpful, the information provided is limited to the coarse granularity at which snapshots can be taken. We present MemInsight, a tool that provides detailed, time-varying analysis of the memory behavior of JavaScript applications, including web applications. MemInsight is platform independent and runs on unmodified JavaScript engines. It employs tuned source-code instrumentation to generate a trace of memory allocations and accesses, and it leverages modern browser features to track precise information for DOM (document object model) objects. It also computes exact object lifetimes without any garbage collector assistance, and exposes this information in an easily-consumable manner for further analysis. We describe several client analyses built into MemInsight, including detection of possible memory leaks and opportunities for stack allocation and object inlining. An experimental evaluation showed that with no modifications to the runtime, MemInsight was able to expose memory issues in several real-world applications.

A range of static analysis tools and techniques have been developed in recent years with the aim of helping JavaScript web application programmers produce code that is more robust, safe, and efficient. However, as shown in a previous large-scale study, many web applications use the JavaScript eval function to dynamically construct code from text strings in ways that obstruct existing static analyses. As a consequence, the analyses either fail to reason about the web applications or produce unsound or useless results.

Current practice in testing JavaScript web applications requires manual construction of test cases, which is difficult and tedious. We present a framework for feedback-directed automated test generation for JavaScript in which execution is monitored to collect information that directs the test generator towards inputs that yield increased coverage. We implemented several instantiations of the framework, corresponding to variations on feedback-directed random testing, in a tool called Artemis. Experiments on a suite of JavaScript applications demonstrate that a simple instantiation of the framework that uses event handler registrations as feedback information produces surprisingly good coverage if enough tests are generated. By also using coverage information and read-write sets as feedback information, a slightly better level of coverage can be achieved, and sometimes with many fewer tests. The generated tests can be used for detecting HTML validity problems and other programming errors.

Developers of JavaScript web applications have little tool support for catching errors early in development. In comparison, an abundance of tools exist for statically typed languages, including sophisticated integrated development environments and specialized static analyses. Transferring such technologies to the domain of JavaScript web applications is challenging. In this paper, we discuss the challenges, which include the dynamic aspects of JavaScript and the complex interactions between JavaScript, HTML, and the browser. From this, we present the first static analysis that is capable of reasoning about the flow of control and data in modern JavaScript applications that interact with the HTML DOM and browser API.