Reflecting on the limitations of a study is a crucial part of the research process. In software engineering studies, this reflection is typically conveyed through discussions of study limitations or threats to validity. In current practice, such discussions seldom provide sufficient insight to understand the rationale for decisions taken before and during the study, and their implications. We revisit the practice of discussing study limitations and threats to validity and identify its weaknesses. We propose to refocus this practice of self-reflection to a discussion centered on the notion of trade-offs . We argue that documenting trade-offs allows researchers to clarify how the benefits of their study design decisions outweigh the costs of possible alternatives. We present guidelines for reporting trade-offs in a way that promotes a fair and dispassionate assessment of researchers’ work.

A large number of tutorials for popular software development technologies are available online, and those about the same technology vary widely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e.resources, differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on theirdistinguishing attributes, i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by itsresource style, i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.

When learning a software technology, programmers face a large variety of resources in different styles and catering to different requirements. Although search engines are helpful to filter relevant resources, programmers are still required to manually go through a number of resources before they find one pertinent to their needs. Prior work has largely concentrated on helping programmers find the precise location of relevant information within a resource. Our work focuses on helping programmers assess the pertinence of resources to differentiate between resources. We investigated how programmers find learning resources online via a diary and interview study, and observed that programmers use certain cues to determine whether to access a resource. Based on our findings, we investigate the extent to which we can support the cue-following process via a prototype tool. Our research supports programmers’ search behaviour for software technology learning resources to inform resource creators on important factors that programmers look for during their search.

In open-source software (OSS), the design of usability is often influenced by the discussions among community members on platforms such as issue tracking systems (ITSs). However, digesting the rich information embedded in issue discussions can be a major challenge due to the vast number and diversity of the comments. We propose and evaluate ArguLens, a conceptual framework and automated technique leveraging an argumentation model to support effective understanding and consolidation of community opinions in ITSs. Through content analysis, we anatomized highly discussed usability issues from a large, active OSS project, into their argumentation components and standpoints. We then experimented with supervised machine learning techniques for automated argument extraction. Finally, through a study with experienced ITS users, we show that the information provided by ArguLens supported the digestion of usability-related opinions and facilitated the review of lengthy issues. ArguLens provides the direction of designing valuable tools for high-level reasoning and effective discussion about usability.

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.

In this paper, we present some construction methods for punctured binary array/sequence pairs (PBAPs/PBSPs) with ideal/optimal correlation constant using their algebraic counterparts punctured difference set pairs in Abelian groups. In addition, we provide new construction techniques of PBAPs/PBSPs via geometry and also using the embeddable sequence pairs of smaller lengths to obtain larger ones. PBAPs/PBSPs find a plethora of applications in radar systems, cryptography, frame synchronization, mismatched filtering, and various other engineering fields.