Using static analysis tools for automating code inspections can be beneficial for software engineers. Such tools can make finding bugs, or software defects, faster and cheaper than manual inspections. Despite the benefits of using static analysis tools to find bugs, research suggests that these tools are underused. In this paper, we investigate why developers are not widely using static analysis tools and how current tools could potentially be improved. We conducted interviews with 20 developers and found that although all of our participants felt that use is beneficial, false positives and the way in which the warnings are presented, among other things, are barriers to use. We discuss several implications of these results, such as the need for an interactive mechanism to help developers fix defects.

/pdf/why-don-t-software-developers-use-static-analysis-tools-to-3f8x2rdcna.pdf

Why don't software developers use static analysis tools to find bugs?

Software security vulnerabilities are one of the critical issues in the realm of computer security. Due to their potential high severity impacts, many different approaches have been proposed in the past decades to mitigate the damages of software vulnerabilities. Machine-learning and data-mining techniques are also among the many approaches to address this issue. In this article, we provide an extensive review of the many different works in the field of software vulnerability analysis and discovery that utilize machine-learning and data-mining techniques. We review different categories of works in this domain, discuss both advantages and shortcomings, and point out challenges and some uncharted territories in the field.

Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey

Static analysis of software with deductive methods is a highly dynamic field of research on the verge of becoming a mainstream technology in software engineering. It consists of a large portfolio of - mostly fully automated - analyses: formal verification, test generation, security analysis, visualization, and debugging. All of them are realized in the state-of-art deductive verification framework KeY. This book is the definitive guide to KeY that lets you explore the full potential of deductive software verification in practice. It contains the complete theory behind KeY for active researchers who want to understand it in depth or use it in their own work. But the book also features fully self-contained chapters on the Java Modeling Language and on Using KeY that require nothing else than familiarity with Java. All other chapters are accessible for graduate students (M.Sc. level and beyond). The KeY framework is free and open software, downloadable from the book companion website which contains also all code examples mentioned in this book.

Deductive Software Verification - The KeY Book

Evolution of software in automated production systems

Context: Open source software (OSS) is changing the way organizations develop, acquire, use, and commercialize software. Objective: This paper seeks to identify how organizations adopt OSS, classify the literature according to these ways of adopting OSS, and with a focus on software development evaluate the research on adoption of OSS in organizations. Method: Based on the systematic literature review method we reviewed publications from 24 journals and seven conference and workshop proceedings, published between 1998 and 2008. From a population of 24,289 papers, we identified 112 papers that provide empirical evidence on how organizations actually adopt OSS. Results: We show that adopting OSS involves more than simply using OSS products. We moreover provide a classification framework consisting of six distinctly different ways in which organizations adopt OSS. This framework is used to illustrate some of the opportunities and challenges organizations meet when approaching OSS, to show that OSS can be adopted successfully in different ways, and to organize and review existing research. We find that existing research on OSS adoption does not sufficiently describe the context of the organizations studied, and it fails to benefit fully from related research fields. While existing research covers a large number of topics, it contains very few closely related studies. To aid this situation, we offer directions for future research. Conclusion: The implications of our findings are twofold. On the one hand, practitioners should embrace the many opportunities OSS offers, but consciously evaluate the consequences of adopting it in their own context. They may use our framework and the success stories provided by the literature in their own evaluations. On the other hand, researchers should align their work, and perform more empirical research on topics that are important to organizations. Our framework may be used to position this research and to describe the context of the organization they are studying.

/pdf/adoption-of-open-source-software-in-software-intensive-4276r9rrtx.pdf

Adoption of open source software in software-intensive organizations - A systematic literature review

Static analysis examines code in the absence of input data and without running the code. It can detect potential security violations (SQL injection), runtime errors (dereferencing a null pointer) and logical inconsistencies (a conditional test that can't possibly be true). Although a rich body of literature exists on algorithms and analytical frameworks used by such tools, reports describing experiences in industry are much harder to come by. The authors describe FindBugs, an open source static-analysis tool for Java, and experiences using it in production settings. FindBugs evaluates what kinds of defects can be effectively detected with relatively simple techniques and helps developers understand how to incorporate such tools into software development.

/pdf/using-static-analysis-to-find-bugs-9haae7lqng.pdf

J.D. Morgenthaler

Papers

Using Static Analysis to Find Bugs