A new model of program dependences for reverse engineering

A dependence model for reverse engineering should treat procedures in a modular fashion and should be fine-grained, distinguishing dependences that are due to different variables. The program dependence graph (PDG) satisfies neither of these criteria. We present a new form of dependence graph that satisfies both, while retaining the advantages of the PDG: it is easy to construct and allows program slicing to be implemented as a simple graph traversal. We define 'chopping', a generalization of slicing that can express most of its variants, and show that, using our dependence graph, it produces more accurate results than algorithms based directly on the PDG.

We present PIDGIN, a program analysis and understanding tool that enables the specification and enforcement of precise application-specific information security guarantees. PIDGIN also allows developers to interactively explore the information flows in their applications to develop policies and investigate counter-examples. PIDGIN combines program dependence graphs (PDGs), which precisely capture the information flows in a whole application, with a custom PDG query language. Queries express properties about the paths in the PDG; because paths in the PDG correspond to information flows in the application, queries can be used to specify global security policies. PIDGIN is scalable. Generating a PDG for a 330k line Java application takes 90 seconds, and checking a policy on that PDG takes under 14 seconds. The query language is expressive, supporting a large class of precise, application-specific security guarantees. Policies are separate from the code and do not interfere with testing or development, and can be used for security regression testing. We describe the design and implementation of PIDGIN and report on using it: (1) to explore information security guarantees in legacy programs; (2) to develop and modify security policies concurrently with application development; and (3) to develop policies based on known vulnerabilities.

A new method for software safety analysis is presented which uses program slicing and constraint solving to construct and analyze path conditions , conditions defined on a program's input variables which must hold for information flow between two points in a program. Path conditions are constructed from subgraphs of a program's dependence graph, specifically, slices and chops. The article describes how constraint solvers can be used to determine if a path condition is satisfiable and, if so, to construct a witness for a safety violation, such as an information flow from a program point at one security level to another program point at a different security level. Such a witness can prove useful in legal matters.The article reviews previous research on path conditions in program dependence graphs; presents new extensions of path conditions for arrays, pointers, abstract data types, and multithreaded programs; presents new decomposition formulae for path conditions; demonstrates how interval analysis and BDDs (binary decision diagrams) can be used to reduce the scalability problem for path conditions; and presents case studies illustrating the use of path conditions in safety analysis. Applying interval analysis and BDDs is shown to overcome the combinatorial explosion that can occur in constructing path conditions. Case studies and empirical data demonstrate the usefulness of path conditions for analyzing practical programs, in particular, how illegal influences on safety-critical programs can be discovered and analyzed.

Automatic program repair (APR) is an effective technique for eliminating defects. The repair of null pointer dereferences, as the most common defects, requires accurate dependencies among statements to determine where to repair and how to repair. In order to precisely identify the data and control dependencies, the program dependency graph is adopted. Based on the symmetry among a large number of patches, we propose four repair mechanisms in this passage, namely the assignment mechanism, restraint mechanism, evading mechanism, and transfer mechanism, and employ the decision tree algorithm to match the best repair mechanism for defects. The four repair mechanisms locate the accurate repair position using the program dependency graph, and generate candidate patches by reassigning the null pointer with an appropriate value, making a judgment for the null value in advance, or throwing an exception. Our method was implemented in the repair tool DTSFix, which supports the automatic repair of null pointer dereference in Java programs. The experimental result on Defects4J shows that 73% of null pointer dereferences are successfully repaired by DTSFix, and that the generated candidate patches do not contain over-fitting patches.

On the impact of multiple source code representations on software engineering tasks — An empirical study

Impact analysis (IA) is a critical software maintenance task that identifies the effects of a given set of code changes on a larger software project with the intention of avoiding potential adverse effects. IA is a cognitively challenging task that involves reasoning about the abstract relationships between various code constructs. Given its difficulty, researchers have worked to automate IA with approaches that primarily use coupling metrics as a measure of the “connectedness” of different parts of a software project. Many of these coupling metrics rely on static, dynamic, or evolutionary information and are based on heuristics that tend to be brittle, require expensive execution analysis, or large histories of co-changes to accurately estimate impact sets. In this paper, we introduce a novel IA approach, called Athena , that combines a software system’s dependence graph information with a conceptual coupling approach that uses advances in deep representation learning for code without the need for change histories and execution information. Previous IA benchmarks are small, containing less than ten software projects, and suffer from tangled commits, making it difficult to measure accurate results. Therefore, we constructed a large-scale IA benchmark, from 25 open-source software projects, that utilizes fine-grained commit information from bug fixes. On this new benchmark, our best performing approach configuration achieves an mRR, mAP, and HIT@10 score of 60.32%, 35.19%, and 81.48%, respectively. Through various ablations and qualitative analyses, we show that Athena ’s novel combination of program dependence graphs and conceptual coupling information leads it to outperform a simpler baseline by 10.34%, 9.55%, and 11.68% with statistical significance.

The internal program representation chosen for a software development environment plays a critical role in the nature of that environment. A form should facilitate implementation and contribute to the responsiveness of the environment to the user. The program dependence graph (PDG) may be a suitable internal form. It allows programs to be sliced in linear time for debugging and for use by language-directed editors. The slices obtained are more accurate than those obtained with existing methods because I/O is accounted for correctly and irrelevant statements on multi-statement lines are not displayed. The PDG may be interpreted in a data driven fashion or may have highly optimized (including vectorized) code produced from it. It is amenable to incremental data flow analysis, improving response time to the user in an interactive environment and facilitating debugging through data flow anomaly detection. It may also offer a good basis for software complexity metrics, adding to the completeness of an environment based on it.

We present an approach to identifying similar code in programs based on finding similar subgraphs in attributed directed graphs. This approach is used on program dependence graphs and therefore considers not only the syntactic structure of programs but also the data flow within (as an abstraction of the semantics). As a result, there is no tradeoff between precision and recall; our approach is very good in both. An evaluation of our prototype implementation shows that the approach is feasible and gives very good results despite the non polynomial complexity of the problem.

This Paper summarizes literature domestic and abroad about the vulnerability evaluation and analyzes the shortcomings of previous evaluation approaches. A novel method based on program dependence graph is presented to implement multi-dimension evaluation on vulnerability. Compared with the previous evaluation models, the new method can access the influence scope and patch fixed situation, which makes the evaluation results complete and accurate proved by the experiment successfully. Finally, we make a conclusion and propose the future research directions in this field.

The program dependence graph, PDG, is used to represent the data and control dependencies between the statements of some program. The data dependencies between the statements are fully understood and they correspond to the definition-use chain. On the other hand the concept of control dependence is not fully explored. In this paper we formalize and analyze the concept or control dependence and present algorithms to find directly the control dependence subgraph of PDG in O(N α(N)), where N is the number of statements in the program.

To assist Java educations, we have developed a Web-based Java Programming Learning Assistant System (JPLAS), that provides the element fill-in-blank problem and the code writing problem. The former problem is designed for novice students to study the grammar and coding style of Java by filling in correct words to the blanks in a given Java code which are marked automatically through checking for coincidences of correct ones. The latter problem is for students to study writing Java codes for given specifications described in natural language that are automatically verified using the test-driven development (TDD) method. Unfortunately, rough transition exists due to different difficulties found between the two problems. In this paper, we propose the statement fill-in-blank problem in JPLAS by asking students to fill in the gap and write one whole statement in a code where the correctness is verified by the TDD method. The blank statement is selected by generating the Program Dependence Graph (PDG) of the code and finding the statement that has the largest dependence in PDG. We verify the effectiveness through applications of the Java programming course in our department.

Several techniques have been developed for identifying similar code fragments in programs. These similar fragments, referred to as code clones, can be used to identify redundant code, locate bugs, or gain insight into program design. Existing scalable approaches to clone detection are limited to finding program fragments that are similar only in their contiguous syntax. Other, semantics-based approaches are more resilient to differences in syntax, such as reordered statements, related statements interleaved with other unrelated statements, or the use of semantically equivalent control structures. However, none of these techniques have scaled to real world code bases. These approaches capture semantic information from Program Dependence Graphs (PDGs), program representations that encode data and control dependencies between statements and predicates. Our definition of a code clone is also based on this representation: we consider program fragments with isomorphic PDGs to be clones. In this paper, we present the first scalable clone detection algorithm based on this definition of semantic clones. Our insight is the reduction of the difficult graph similarity problem to a simpler tree similarity problem by mapping carefully selected PDG subgraphs to their related structured syntax. We efficiently solve the tree similarity problem to create a scalable analysis. We have implemented this algorithm in a practical tool and performed evaluations on several million-line open source projects, including the Linux kernel. Compared with previous approaches, our tool locates significantly more clones, which are often more semantically interesting than simple copied and pasted code fragments.

The single-program multiple-data (SPMD) mode of execution is an effective approach for exploiting parallelism in programs written using the shared-memory programming model on distributed memory machines. However, during SPMD execution one must consider dependencies due to the transfer of data among the processors. Such dependencies can be avoided by reordering the communication operations (sends and receives). However, no formal framework has been developed to explicitly recognize the represent such dependencies. The author identifies two types of dependencies, namely communication dependencies and scheduling dependencies, and proposes to represent these dependencies explicitly in the program dependency graph. Next, he presents program transformations that use this dependency information in transforming the program and increasing the degree of parallelism exploited. Finally, the author presents program transformations that reduce communication related run-time overhead. >

With the significant increase of computer and Internet based crimes, it becomes increasingly important to have techniques that can be applied in a legal setting to assist the court in making judgements about malware, theft of code and computer fraud. To better deal with author identification of software, we propose a semantic approach to identifying authorship through the comparison of program data flows. To do so, we compute program dependences, compute program similarity if detecting theft of code is needed, and thus query about not only the syntactic structure of programs but also the data flow within in order to discriminate authors. The experimental result reveals that our technique is more robust even with some intentional code modifications.

Existing studies have shown that structural dependencies within code are good predictors for code actual change impact set—a set of entities that repeatedly changing together to ensure a consistent and complete change. However, the result is far from ideal, particularly when insufficient historical data are available at an early stage of software development. This paper demonstrates that a better understanding of data dependencies in addition to call dependencies greatly improves actual change impact set prediction. We propose a new approach and tool (namely, CHIP) to predict software actual change impact sets leveraging both call and data sharing dependencies. For this purpose, CHIP employs novel extensions (dependency frequency filtering and shared data type idf filtering) to reduce false positives. CHIP assumes that developers know initial places where to start making changes in the source code even though they may not know all changes. This approach has been empirically evaluated on 4 large‐scale open source systems. Our evaluation demonstrates that data sharing dependencies have a complementary impact on software actual change impact set prediction as compared with predictions based on call dependencies only. CHIP improves the F2‐score compared with the predictors using both Program Dependence Graph and evolutionary couplings.