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The Guide to the Software Engineering Body of Knowledge Reporting on the SWEBOK pr o ject, t he authors—who re pr e sent the pr o ject's editorial team—discuss the thr e e-phase plan to characterize a body of knowledge, a vital step toward developing softwar e engineering as a pr o fession.

The classical method for identifying cause-effect relationships is to conduct controlled experiments. This paper reports upon the present state of how controlled experiments in software engineering are conducted and the extent to which relevant information is reported. Among the 5,453 scientific articles published in 12 leading software engineering journals and conferences in the decade from 1993 to 2002, 103 articles (1.9 percent) reported controlled experiments in which individuals or teams performed one or more software engineering tasks. This survey quantitatively characterizes the topics of the experiments and their subjects (number of subjects, students versus professionals, recruitment, and rewards for participation), tasks (type of task, duration, and type and size of application) and environments (location, development tools). Furthermore, the survey reports on how internal and external validity is addressed and the extent to which experiments are replicated. The gathered data reflects the relevance of software engineering experiments to industrial practice and the scientific maturity of software engineering research.

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A survey of controlled experiments in software engineering

Many of the existing approaches in Software Comprehension focus on program structure or external documentation. However, by analyzing formal information the informal semantics contained in the vocabulary of source code are overlooked. To understand software as a whole, we need to enrich software analysis with the developer knowledge hidden in the code naming. This paper proposes the use of information retrieval to exploit linguistic information found in source code, such as identifier names and comments. We introduce Semantic Clustering, a technique based on Latent Semantic Indexing and clustering to group source artifacts that use similar vocabulary. We call these groups semantic clusters and we interpret them as linguistic topics that reveal the intention of the code. We compare the topics to each other, identify links between them, provide automatically retrieved labels, and use a visualization to illustrate how they are distributed over the system. Our approach is language independent as it works at the level of identifier names. To validate our approach we applied it on several case studies, two of which we present in this paper. Note: Some of the visualizations presented make heavy use of colors. Please obtain a color copy of the article for better understanding.

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Semantic clustering: Identifying topics in source code

We introduce Sapienz, an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. Sapienz combines random fuzzing, systematic and search-based exploration, exploiting seeding and multi-level instrumentation. Sapienz significantly outperforms (with large effect size) both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, in 7/10 experiments for coverage, 7/10 for fault detection and 10/10 for fault-revealing sequence length. When applied to the top 1,000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. So far we have managed to make contact with the developers of 27 crashing apps. Of these, 14 have confirmed that the crashes are caused by real faults. Of those 14, six already have developer-confirmed fixes.

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Sapienz: multi-objective automated testing for Android applications

In this paper, we examine the state of software engineering (SE) research from the point of view of the following research questions: 1. What topics do SE researchers address? 2. What research approaches do SE researchers use? 3. What research methods do SE researchers use? 4. On what reference disciplines does SE research depend? 5. At what levels of analysis do SE researchers conduct research? To answer those questions, we examined 369 papers in six leading research journals in the SE field, answering those research questions for each paper. From that examination, we conclude that SE research is diverse regarding topic, narrow regarding research approach and method, inwardly-focused regarding reference discipline, and technically focused (as opposed to behaviorally focused) regarding level of analysis. We pass no judgment on the SE field as a result of these findings. Instead, we present them as groundwork for future SE research efforts.

Research in software engineering: an analysis of the literature

data types Sorting and searching parallel and distributed algorithms 3. [AR] Computer Architecture

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A Straw

Papers

Guide to the Software Engineering Body of Knowledge