When building enterprise applications on Java frameworks (e.g., Spring), developers often specify components and configure operations with a special kind of XML files named “deployment descriptors (DD)”. Maintaining such XML files is challenging and time-consuming; because (1) the correct configuration semantics is domain-specific but usually vaguely documented, and (2) existing compilers and program analysis tools rarely examine XML files. To help developers ensure the quality of DD, this paper presents a novel approach—Xeditor—that extracts configuration couplings (i.e., frequently co-occurring configurations) from DD, and adopts the coupling rules to validate new or updated files.

Xeditor has two phases: coupling extraction and bug detection. To identify couplings, Xeditor first mines DD in open-source projects, and extracts XML entity pairs that (i) frequently co-exist in the same files and (ii) hold the same data at least once. Xeditor then conducts customized association rule mining based on the extracted pairs. For bug detection, given a program commit, Xeditor checks whether any new or updated XML file violates the identified couplings; if so, Xeditor reports the violation(s).

We applied Xeditor to 4,248 DD, which were mined from 1,137 GitHub projects. Based on our manual inspection, Xeditor extracted couplings with high precision (73%). Furthermore, we built a ground truth data set based on the manual inspection results, and conducted 10-fold cross validation to evaluate Xeditor ’s effectiveness of bug detection. On average, Xeditor detected bugs with 92% precision, 96% recall, and 94% accuracy. Finally, we applied Xeditor to the version history of another 478 GitHub projects. Xeditor identified 26 really erroneous XML updates, 15 of which match the program changes later committed by developers.

Chengyuan Wen

Virginia Tech

Yaxuan Zhang

Virginia Tech

Xiao He

University of Science and Technology Beijing, China

Na Meng

Virginia Tech

United States

Understanding the influence of configuration options on performance is key for finding an optimal system configuration, system understanding, and performance debugging. In prior research, a number of performance influence modeling approaches have been proposed, which all assign scalar values to option influences and model predictions. However, these point estimates falsely imply a certainty regarding an option’s influence that neglects several sources of uncertainty within the assessment process, such as (1) measurement bias, (2) model representation and learning process, and (3) incomplete data. This leads to the situation that different approaches assign different scalar performance values to options and interactions among them. The true influence is uncertain, though, there is no way to even quantify this uncertainty with state-of-the-art performance modeling approaches.

We propose a novel approach based on probabilistic programming that explicitly models uncertainty for option influences and consequently provides a confidence interval for each prediction alongside a scalar. This way, we can explain, for the first time, why predictions may cause errors and which option’s influences may be unreliable. Our evaluation on 10 real-world subject systems shows that with our implementation, P4, we yield errors that match the state of the art when considering only the scalar component of the prediction, and we achieve competitive accuracies while providing reliable confidence intervals.

Johannes Dorn

Leipzig University

Sven Apel

Saarland University, Germany

Germany

Norbert Siegmund

Leipzig University

In Open Source Software (OSS) projects, pre-built tools dominate DevOps-oriented pipelines. In practice, a multitude of configuration management, cloud-based continuous integration, and automated deployment tools exist, and often more than one for each task. Tools are adopted and given up by OSS projects regularly. Prior work has shown that some tool adoptions are preceded by discussions and that tool adoptions can result in benefits to the project. But important questions remain: how do teams decide to adopt a tool? What is discussed before the adoption and for how long? And, what team characteristics are determinant of the adoption?

In this paper, we employ a large-scale, mixed-method empirical study in order to characterize the team discussions and to discern the team-level determinants of tool adoption into an OSS projects’ development pipelines. Guided by theories of team and individual motivations and dynamics, we perform exploratory data analyses, do deep-dive case studies, and develop regression models to learn the determinants of adoption and discussion length, and the direction of their effect on the adoption. From data of commit and comment traces of large-scale GitHub projects, our models find that prior exposure to a tool and member involvement are positively associated with the tool adoption, while longer discussions and the number of newer team members are negatively associated. These results can provide guidance beyond the technical appropriateness for the timeliness of tool adoptions in diverse programmer teams.

Likang Yin

University of California, Davis

Vladimir Filkov

University of California at Davis, USA