Today’s small and medium-sized enterprises (SMEs) in the software industry are faced with major challenges. While having to work efficiently using limited resources they have to perform quality assurance on their code to avoid the risk of further effort for bug fixes or compensations. Automated static analysis can reduce this risk because it promises little effort for running an analysis. We report on our experience in analysing five projects from and with SMEs by three different static analysis techniques: code clone detection, bug pattern detection and architecture conformance analysis. We found that the effort that was needed to introduce those techniques was small (mostly below one person-hour), that we can detect diverse defects in production code and that the participating companies perceived the usefulness of the presented techniques as well as our analysis results high enough to include the techniques in their quality assurance.
Hofer @cite_12 states that only 10 Returning to automated static analysis techniques, to the best of our knowledge, multiple techniques have never been applied in a study in an SME context. However, there are several publications in which such techniques were investigated separately and in other contexts:
@cite_12
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Automated static analysis is a promising technique to detect defects in software. However, although considerable effort has been spent for developing sophisticated detection possibilities, the effectiveness and efficiency has not been treated in equal detail. This paper presents the results of two industrial case studies in which two tools based on bug patterns for Java are applied and evaluated. First, the economic implications of the tools are analysed. It is estimated that only 3-4 potential field defects need to be detected for the tools to be cost-efficient. Second, the capabilities of detecting field defects are investigated. No field defects have been found that could have been detected by the tools. Third, the identification of fault-prone classes based on the results of such tools is investigated and found to be possible. Finally, methodological consequences are derived from the results and experiences in order to improve the use of bug pattern tools in practice. Static checking can verify the absence of errors in a program, but often requires written annotations or specifications. As a result, static checking can be difficult to use effectively: it can be difficult to determine a specification and tedious to annotate programs. Automated tools that aid the annotation process can decrease the cost of static checking and enable it to be more widely used.This paper describes an evaluation of the effectiveness of two techniques, one static and one dynamic, to assist the annotation process. We quantitatively and qualitatively evaluate 41 programmers using ESC Java in a program verification task over three small programs, using Houdini for static inference and Daikon for dynamic inference. We also investigate the effect of unsoundness in the dynamic analysis.Statistically significant results show that both inference tools improve task completion; Daikon enables users to express more correct invariants; unsoundness of the dynamic analysis is little hindrance to users; and users imperfectly exploit Houdini. Interviews indicate that beginning users found Daikon to be helpful; Houdini to be neutral; static checking to be of potential practical use; and both assistance tools to have unique benefits.Our observations not only provide a critical evaluation of these two techniques, but also highlight important considerations for creating future assistance tools. Static analysis is an important part of today's quality assurance process. It can be performed manually, by means of code reviews, or automatically, by automated static analysis tools (ASATs). However, there is still much unknown about the state of static analysis. This includes hard data on how prevalent static analysis is among projects. And while there have been studies on how projects use code reviews, current research has not investigated how developers configure the ASATs that they use and how these configurations evolve. In this thesis, we answer these questions by means of a large scale analysis of open source software. We found that both code reviews and ASATs are common, but not ubiquitous. Many projects do not perform code reviews for the changes of core developers and do not enforce a strict use of ASATs. Regarding the use of ASATs, developers both use and avoid maintainability defects to a greater extent than functional defects. Most configurations of developers deviate from the default and hardly contain custom rules. However, there are few default rules that are changed by a significant percentage of developers. Finally, most configuration files never change. And if they do, the changes are small, occur over the lifetime of the project, and are not triggered by ASAT version updates. No single software fault-detection technique is capable of addressing all fault-detection concerns. Similarly to software reviews and testing, static analysis tools (or automated static analysis) can be used to remove defects prior to release of a software product. To determine to what extent automated static analysis can help in the economic production of a high-quality product, we have analyzed static analysis faults and test and customer-reported failures for three large-scale industrial software systems developed at Nortel Networks. The data indicate that automated static analysis is an affordable means of software fault detection. Using the orthogonal defect classification scheme, we found that automated static analysis is effective at identifying assignment and checking faults, allowing the later software production phases to focus on more complex, functional, and algorithmic faults. A majority of the defects found by automated static analysis appear to be produced by a few key types of programmer errors and some of these types have the potential to cause security vulnerabilities. Statistical analysis results indicate the number of automated static analysis faults can be effective for identifying problem modules. Our results indicate static analysis tools are complementary to other fault-detection techniques for the economic production of a high-quality software product.