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Context Software systems are often shipped with defects. Whenever a bug is reported, developers use the information available in the associated report to locate source code fragments that need to be modified in order to fix the bug. However, as software systems evolve in size and complexity, bug localization can become a tedious and time-consuming process. To minimize the manual effort, contemporary bug localization tools utilize Information Retrieval (IR) methods for automated support. IR methods exploit the textual content of bug reports to automatically capture and rank relevant buggy source files. Objective In this paper, we propose a new paradigm of information-theoretic IR methods to support bug localization tasks in software systems. These methods, including Pointwise Mutual Information (PMI) and Normalized Google Distance (NGD), exploit the co-occurrence patterns of code terms in the software system to reveal hidden textual semantic dimensions that other methods often fail to capture. Our objective is establish accurate semantic similarity relations between source code and bug reports. Method Five benchmark datasets from different application domains are used to conduct our analysis. The proposed methods are compared against classical IR methods that are commonly used in bug localization research. Results The results show that information-theoretic IR methods significantly outperform classical IR methods, providing a semantically aware, yet, computationally efficient solution for bug localization in large and complex software systems. (A replication package is available at: http://seel.cse.lsu.edu/data/ist17.zip ). Conclusions Information-theoretic co-occurrence methods provide “just enough semantics” necessary to establish relations between bug reports and code artifacts, achieving a balance between simple lexical methods and computationally-expensive semantic IR methods that require substantial amounts of data to function properly.

Context: Mobile applications (apps) have become deeply personal, constantly demanding access to privacy-sensitive information in exchange for more personalized user experiences. Such privacy-invading practices have generated major multidimensional privacy concerns among app users. Objective: The research on mobile app privacy has experienced rapid growth over the past decade. This line of research is aimed at systematically exposing the privacy practices of apps and proposing solutions to protect the privacy of mobile app users. In this paper, we conduct a systematic mapping study of this body of research. Our objectives are to a) explore trends in SE app privacy research, b) categorize existing evidence, and c) identify potential directions for future research. Method: A systematic mapping study of 59 Software Engineering (SE) primary studies on mobile app privacy. Our scope is studies published in software engineering venues between 2008 and 2018. Results: Our results show that existing literature can be divided into four main categories: privacy policy, requirements, user perspective, and leak detection. Furthermore, our survey reveals an imbalance between these categories—the majority of existing research focuses on proposing tools for detecting privacy leaks, with fewer studies targeting privacy requirements and policy and even fewer on user perspective. Conclusions: Our survey exposes several gaps in existing research and suggests areas for improvement.