OpenJML: Software verification for Java 7 using JML, OpenJDK, and Eclipse

OpenJML is a tool for checking code and specifications of Java programs. We describe our experience building the tool on the foundation of JML, OpenJDK and Eclipse, as well as on many advances in specification-based software verification. The implementation demonstrates the value of integrating specification tools directly in the software development IDE and in automating as many tasks as possible. The tool, though still in progress, has now been used for several college-level courses on software specification and verification and for small-scale studies on existing Java programs.Comment: In Proceedings F-IDE 2014, arXiv:1404.578

A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

SPEST - A TOOL FOR SPECIFICATION-BASED TESTING

This thesis presents a tool for SPEcification based teSTing (SPEST). SPEST is designed to use well known practices for automated black-box testing to reduce the burden of testing on developers. The tool uses a simple formal specification language to generate highly-readable unit tests that embody best practices for thorough software testing. Because the specification language used to generate the assertions about the code can be compiled, it can also be used to ensure that documentation describing the code is maintained during development and refactoring. The utility and effectiveness of SPEST were validated through several exper- iments conducted with students in undergraduate software engineering classes. The first experiment compared the understandability and efficiency of SPEST generated tests against student written tests based on the Java Modeling Lan- guage (JML)[25] specifications. JML is a widely used language for behavior program specification. A second experiment evaluated readability through a sur- vey comparing SPEST generated tests against tests written by well established software developers. The results from the experiments showed that SPEST’s specification language is at least understandable as JML, SPEST’s specification language is more readable than JML, and strongly suggest that SPEST is capable of reducing the effort required to produce effective tests

Proceedings of the Resolve Workshop 2006

The aim of the RESOLVE Workshop 2006 was to bring together researchers and educators interested in: Refining formal approaches to software engineering, especially component-based systems, and introducing them into the classroom. The workshop served as a forum for participants to present and discuss recent advances, trends, and concerns in these areas, as well as formulate a common understanding of emerging research issues and possible solution paths

Fast and Lean Immutable Multi-Maps on the JVM based on Heterogeneous Hash-Array Mapped Tries

An immutable multi-map is a many-to-many thread-friendly map data structure with expected fast insert and lookup operations. This data structure is used for applications processing graphs or many-to-many relations as applied in static analysis of object-oriented systems. When processing such big data sets the memory overhead of the data structure encoding itself is a memory usage bottleneck. Motivated by reuse and type-safety, libraries for Java, Scala and Clojure typically implement immutable multi-maps by nesting sets as the values with the keys of a trie map. Like this, based on our measurements the expected byte overhead for a sparse multi-map per stored entry adds up to around 65B, which renders it unfeasible to compute with effectively on the JVM. In this paper we propose a general framework for Hash-Array Mapped Tries on the JVM which can store type-heterogeneous keys and values: a Heterogeneous Hash-Array Mapped Trie (HHAMT). Among other applications, this allows for a highly efficient multi-map encoding by (a) not reserving space for empty value sets and (b) inlining the values of singleton sets while maintaining a (c) type-safe API. We detail the necessary encoding and optimizations to mitigate the overhead of storing and retrieving heterogeneous data in a hash-trie. Furthermore, we evaluate HHAMT specifically for the application to multi-maps, comparing them to state-of-the-art encodings of multi-maps in Java, Scala and Clojure. We isolate key differences using microbenchmarks and validate the resulting conclusions on a real world case in static analysis. The new encoding brings the per key-value storage overhead down to 30B: a 2x improvement. With additional inlining of primitive values it reaches a 4x improvement

JSKETCH: Sketching for Java

Sketch-based synthesis, epitomized by the SKETCH tool, lets developers synthesize software starting from a partial program, also called a sketch or template. This paper presents JSKETCH, a tool that brings sketch-based synthesis to Java. JSKETCH's input is a partial Java program that may include holes, which are unknown constants, expression generators, which range over sets of expressions, and class generators, which are partial classes. JSKETCH then translates the synthesis problem into a SKETCH problem; this translation is complex because SKETCH is not object-oriented. Finally, JSKETCH synthesizes an executable Java program by interpreting the output of SKETCH.Comment: This research was supported in part by NSF CCF-1139021, CCF- 1139056, CCF-1161775, and the partnership between UMIACS and the Laboratory for Telecommunication Science

Refactoring for parameterizing Java classes

Type safety and expressiveness of many existing Java libraries and theirclient applications would improve, if the libraries were upgraded to definegeneric classes. Efficient and accurate tools exist to assist clientapplications to use generics libraries, but so far the libraries themselvesmust be parameterized manually, which is a tedious, time-consuming, anderror-prone task. We present a type-constraint-based algorithm forconverting non-generic libraries to add type parameters. The algorithmhandles the full Java language and preserves backward compatibility, thusmaking it safe for existing clients. Among other features, it is capableof inferring wildcard types and introducing type parameters formutually-dependent classes. We have implemented the algorithm as a fullyautomatic refactoring in Eclipse.We evaluated our work in two ways. First, our tool parameterized code thatwas lacking type parameters. We contacted the developers of several ofthese applications, and in all cases where we received a response, theyconfirmed that the resulting parameterizations were correct and useful.Second, to better quantify its effectiveness, our tool parameterizedclasses from already-generic libraries, and we compared the results tothose that were created by the libraries' authors. Our tool performed therefactoring accurately -- in 87% of cases the results were as good as thosecreated manually by a human expert, in 9% of cases the tool results werebetter, and in 4% of cases the tool results were worse