Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements engineering is eligible for multiple reasons: (1) requirements artifacts are primarily written in natural language, (2) causal sentences convey essential context about the subject of requirements, and (3) extracted and formalized causality relations are usable for a (semi-)automatic translation into further artifacts, such as test cases. Objective: We aim at understanding the value of interactive causality extraction based on syntactic criteria for the context of requirements engineering. Method: We developed a prototype of a system for automatic causality extraction and evaluate it by applying it to a set of publicly avail-able requirements artifacts, determining whether the automatic extraction reduces the manual effort of requirements formalization. Result: During the evaluation, we analyzed 2373 natural language sentences from 13 requirements documents, 282 of which were causal (11.88%). The best evaluation of a requirements document provided an automatic extraction of 7.2 of 14 cause-effect graphs on average (51.42%), which demonstrates the feasibility of the approach. Limitation: The feasibility of the approach has been proven in theory but actual human interaction with the system has been disregarded so far. Evaluating the applicability of the automatic causality ex-traction for a requirements engineer is left for future research. Conclusion: A syntactic approach for causality extraction is viable for the context of requirements engineering and can aid a pipeline towards an automatic generation of further artifacts, like test cases, from requirements artifacts.

Julian Frattini

Blekinge Institute of Technology

Sweden

Maximilian Junker

Technische Universität Muenchen

Michael Unterkalmsteiner

Blekinge Institute of Technology

Sweden

Daniel Mendez

Blekinge Institute of Technology

Sweden

Cross-Project Defect Prediction (CPDP), which borrows data from similar projects by combining a transfer learner with a classifier, have emerged as a promising way to predict software defects when the available data about the target project is insufficient. However, developing such a model is challenge because it is difficult to determine the right combination of transfer learner and classifier along with their optimal hyper-parameter settings. In this paper, we propose a tool, dubbed BiLO-CPDP, which is the first of its kind to formulate the automated CPDP model discovery from the perspective of bi-level programming. In particular, the bi-level programming proceeds the optimization with two nested levels in a hierarchical manner. Specifically, the upper-level optimization routine is designed to search for the right combination of transfer learner and classifier while the nested lower-level optimization routine aims to optimize the corresponding hyper-parameter settings. To evaluate BiLO-CPDP, we conduct experiments on 20 projects to compare it with a total of 21 existing CPDP techniques, along with its single-level optimization variant and Auto-Sklearn, a state-of-the-art automated machine learning tool. Empirical results show that BiLO-CPDP champions better prediction performance than all other 21 existing CPDP techniques on 70% of the projects, while being overwhelmingly superior to Auto-Sklearn and its single-level optimization variant on all cases. Furthermore, the unique bi-level formalization in BiLO-CPDP also permits to allocate more budget to the upper-level, which significantly boosts the performance.

Ke Li

University of Exeter

United Kingdom

Zilin Xiang

University of Electronic Science and Technology of China

Tao Chen

Loughborough University

United Kingdom

Kay Chen Tan

City University of Hong Kong

Code comments are valuable for program comprehension and software maintenance, and also require maintenance with code evolution. However, when changing code, developers sometimes neglect updating the related comments, bringing in inconsistent or obsolete comments (aka., bad comments). Such comments are detrimental since they may mislead developers and lead to future bugs. Therefore, it is necessary to fix and avoid bad comments. In this work, we argue that bad comments can be reduced and even avoided by automatically performing comment updates with code changes. We refer to this task as “Just-In-Time (JIT) Comment Updating” and propose an approach named CUP (Comment UPdater) to automate this task. CUP can be used to assist developers in updating comments during code changes and can consequently help avoid the introduction of bad comments. Specifically, CUP leverages a novel neural sequence-to-sequence model to learn comment update patterns from extant code-comment co-changes and can automatically generate a new comment based on its corresponding old comment and code change. Several customized enhancements, such as a special tokenizer and a novel co-attention mechanism, are introduced in CUP by us to handle the characteristics of this task. We build a dataset with over 108K comment-code co-change samples and evaluate CUP on it. The evaluation results show that CUP outperforms an information-retrieval-based and a rule-based baselines by substantial margins, and can reduce developers’ edits required for JIT comment updating. In addition, the comments generated by our approach are identical to those updated by developers in 1612 (16.7%) test samples, 7 times more than the best-performing baseline.

Zhongxin Liu

Zhejiang University

China

Xin Xia

Monash University

Australia

Meng Yan

Chongqing University

Shanping Li

Zhejiang University