Precise set sharing and nullity analysis for java-style program

Finding useful sharing information between instances in object- oriented programs has been recently the focus of much research. The applications of such static analysis are multiple: by knowing which variables share in memory we can apply conventional compiler optimizations, find coarse-grained parallelism opportunities, or, more importantly,erify certain correctness aspects of programs even in the absence of annotations In this paper we introduce a framework for deriving precise sharing information based on abstract interpretation for a Java-like language. Our analysis achieves precision in various ways. The analysis is multivariant, which allows separating different contexts. We propose a combined Set Sharing + Nullity + Classes domain which captures which instances share and which ones do not or are definitively null, and which uses the classes to refine the static information when inheritance is present. Carrying the domains in a combined way facilitates the interaction among the domains in the presence of mutivariance in the analysis. We show that both the set sharing part of the domain as well as the combined domain provide more accurate information than previous work based on pair sharing domains, at reasonable cost

Precise set sharin analysis for java-style programs (and proofs).

Finding useful sharing information between instances in object- oriented programs has recently been the focus of much research. The applications of such static analysis are multiple: by knowing which variables definitely do not share in memory we can apply conventional compiler optimizations, find coarse-grained parallelism opportunities, or, more importantly, verify certain correctness aspects of programs even in the absence of annotations. In this paper we introduce a framework for deriving precise sharing information based on abstract interpretation for a Java-like language. Our analysis achieves precision in various ways, including supporting multivariance, which allows separating different contexts. We propose a combined Set Sharing + Nullity + Classes domain which captures which instances do not share and which ones are definitively null, and which uses the classes to refine the static information when inheritance is present. The use of a set sharing abstraction allows a more precise representation of the existing sharings and is crucial in achieving precision during interprocedural analysis. Carrying the domains in a combined way facilitates the interaction among them in the presence of multivariance in the analysis. We show through examples and experimentally that both the set sharing part of the domain as well as the combined domain provide more accurate information than previous work based on pair sharing domains, at reasonable cost

Conceptual roles of data in program: analyses and applications

Program comprehension is the prerequisite for many software evolution and maintenance tasks. Currently, the research falls short in addressing how to build tools that can use domain-specific knowledge to provide powerful capabilities for extracting valuable information for facilitating program comprehension. Such capabilities are critical for working with large and complex program where program comprehension often is not possible without the help of domain-specific knowledge.;Our research advances the state-of-art in program analysis techniques based on domain-specific knowledge. The program artifacts including variables and methods are carriers of domain concepts that provide the key to understand programs. Our program analysis is directed by domain knowledge stored as domain-specific rules. Our analysis is iterative and interactive. It is based on flexible inference rules and inter-exchangeable and extensible information storage. We designed and developed a comprehensive software environment SeeCORE based on our knowledge-centric analysis methodology. The SeeCORE tool provides multiple views and abstractions to assist in understanding complex programs. The case studies demonstrate the effectiveness of our method. We demonstrate the flexibility of our approach by analyzing two legacy programs in distinct domains

Enforcing Abstract Immutability

Researchers have recently proposed a number of systems for expressing, verifying, and inferring immutability declarations. These systems are often rigid, and do not support "abstract immutability". An abstractly immutable object is an object o which is immutable from the point of view of any external methods. The C++ programming language is not rigid–it allows developers to express intent by adding immutability declarations to methods. Abstract immutability allows for performance improvements such as caching, even in the presence of writes to object fields. This dissertation presents a system to enforce abstract immutability. First, we explore abstract immutability in real-world systems. We found that developers often incorrectly use abstract immutability, perhaps because no programming language helps developers correctly implement abstract immutability. We believe that this omission leads to incorrect usages. Specifically, we wrote a dynamic analysis that reports any writes through immutability declarations. To our knowledge, this work was the first to explore how objects implement abstract immutability (or fail to implement it). Our novel study found three uses of abstract immutability: caching, delayed initialization, and unit testing. Unit testing was a surprising application of abstract immutability, and we believe that the ability to modify state is needed for proper unit testing. Next, we explore developers' revealed needs for immutability in the source code. We found that the majority of classes contain a mix of immutable and mutable methods, with a majority of the overall methods being immutable. Immutability systems with only immutable or all-mutating classes are insufficient: developers need immutability declarations at method granularity. Our study then combined developer immutability declarations with results from a static analysis to estimate the true number of immutable methods. The static analysis checked that no transitive writes to a receiver object occurred. Our results indicated the need for a sophisticated analysis to check that these apparently abstractly immutable methods were indeed abstractly immutable. Finally, we created a novel static analysis which checks that developers follow abstract immutability. Specifically, we define abstract immutability to mean that a class's set of immutable methods is collectively free of writes to exposed fields. Our analysis found incorrect usages of abstract immutability, such as incorrect caching. This analysis is particularly valuable in the context of code evolution, whereby subsequent programmers may make changes that break previously-correct cache implementations, for instance. Our work allows developers to trust that their code is abstractly immutable

Impact analysis of database schema changes

When database schemas require change, it is typical to predict the effects of the change, first to gauge if the change is worth the expense, and second, to determine what must be reconciled once the change has taken place. Current techniques to predict the effects of schema changes upon applications that use the database can be expensive and error-prone, making the change process expensive and difficult. Our thesis is that an automated approach for predicting these effects, known as an impact analysis, can create a more informed schema change process, allowing stakeholders to obtain beneficial information, at lower costs than currently used industrial practice. This is an interesting research problem because modern data-access practices make it difficult to create an automated analysis that can identify the dependencies between applications and the database schema. In this dissertation we describe a novel analysis that overcomes these difficulties. We present a novel analysis for extracting potential database queries from a program, called query analysis. This query analysis builds upon related work, satisfying the additional requirements that we identify for impact analysis. The impacts of a schema change can be predicted by analysing the results of query analysis, using a process we call impact calculation. We describe impact calculation in detail, and show how it can be practically and efficiently implemented. Due to the level of accuracy required by our query analysis, the analysis can become expensive, so we describe existing and novel approaches for maintaining an efficient and computational tractable analysis. We describe a practical and efficient prototype implementation of our schema change impact analysis, called SUITE. We describe how SUITE was used to evaluate our thesis, using a historical case study of a large commercial software project. The results of this case study show that our impact analysis is feasible for large commercial software applications, and likely to be useful in real-world software development

Subheap-Augmented Garbage Collection

Automated memory management avoids the tedium and danger of manual techniques. However, as no programmer input is required, no widely available interface exists to permit principled control over sometimes unacceptable performance costs. This dissertation explores the idea that performance-oriented languages should give programmers greater control over where and when the garbage collector (GC) expends effort. We describe an interface and implementation to expose heap partitioning and collection decisions without compromising type safety. We show that our interface allows the programmer to encode a form of reference counting using Hayes\u27 notion of key objects. Preliminary experimental data suggests that our proposed mechanism can avoid high overheads suffered by tracing collectors in some scenarios, especially with tight heaps. However, for other applications, the costs of applying subheaps---in human effort and runtime overheads---remain daunting

Coherent Dependence Cluster

This thesis introduces coherent dependence clusters and shows their relevance in areas of software engineering such as program comprehension and mainte- nance. All statements in a coherent dependence cluster depend upon the same set of statements and affect the same set of statements; a coherent cluster’s statements have ‘coherent’ shared backward and forward dependence. We introduce an approximation to efficiently locate coherent clusters and show that its precision significantly improves over previous approximations. Our empirical study also finds that, despite their tight coherence constraints, coherent dependence clusters are to be found in abundance in production code. Studying patterns of clustering in several open-source and industrial programs reveal that most contain multiple significant coherent clusters. A series of case studies reveal that large clusters map to logical functionality and pro- gram structure. Cluster visualisation also reveals subtle deficiencies of program structure and identify potential candidates for refactoring efforts. Supplemen- tary studies of inter-cluster dependence is presented where identification of coherent clusters can help in deriving hierarchical system decomposition for reverse engineering purposes. Furthermore, studies of program faults find no link between existence of coherent clusters and software bugs. Rather, a longi- tudinal study of several systems find that coherent clusters represent the core architecture of programs during system evolution. Due to the inherent conservativeness of static analysis, it is possible for unreachable code and code implementing cross-cutting concerns such as error- handling and debugging to link clusters together. This thesis studies their effect on dependence clusters by using coverage information to remove unexecuted and rarely executed code. Empirical evaluation reveals that code reduction yields smaller slices and clusters