Towards Automating Code Review Activities Tufano, Rosalia; Pascarella, Luca; Tufano, Michele ...
2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE),
05/2021
Conference Proceeding
Odprti dostop
Code reviews are popular in both industrial and open source projects. The benefits of code reviews are widely recognized and include better code quality and lower likelihood of introducing bugs. ...However, since code review is a manual activity it comes at the cost of spending developers' time on reviewing their teammates' code.
Our goal is to make the first step towards partially automating the code review process, thus, possibly reducing the manual costs associated with it. We focus on both the contributor and the reviewer sides of the process, by training two different Deep Learning architectures. The first one learns code changes performed by developers during real code review activities, thus providing the contributor with a revised version of her code implementing code transformations usually recommended during code review before the code is even submitted for review. The second one automatically provides the reviewer commenting on a submitted code with the revised code implementing her comments expressed in natural language.
The empirical evaluation of the two models shows that, on the contributor side, the trained model succeeds in replicating the code transformations applied during code reviews in up to 16% of cases. On the reviewer side, the model can correctly implement a comment provided in natural language in up to 31% of cases. While these results are encouraging, more research is needed to make these models usable by developers.
Mobile devices have become a permanent fixture in modern society. As such, it is of critical importance that the mobile development process is made as frictionless as possible to facilitate the ...creation of high-quality apps for end users. This keynote offers a brief introduction to mobile development paradigms, surveys the major categories of research conducted to date towards improving mobile software engineering, examines open challenges, and outlines a roadmap of future work aimed to support mobile developers.
A Multi-Study Investigation into Dead Code Romano, Simone; Vendome, Christopher; Scanniello, Giuseppe ...
IEEE transactions on software engineering,
2020-Jan.-1, 2020-1-1, 20200101, Letnik:
46, Številka:
1
Journal Article
Recenzirano
Dead code is a bad smell and it appears to be widespread in open-source and commercial software systems. Surprisingly, dead code has received very little empirical attention from the software ...engineering research community. In this paper, we present a multi-study investigation with an overarching goal to study, from the perspective of researchers and developers, when and why developers introduce dead code, howthey perceive and cope with it, and whether dead code is harmful. To this end, we conducted semi-structured interviews with software professionals and four experiments at the University of Basilicata and the College of William & Mary. The results suggest that it is worth studying dead code not only in the maintenance and evolution phases, where our results suggest that dead code is harmful, but also in the design and implementation phases. Our results motivate future work to develop techniques for detecting and removing dead code and suggest that developers should avoid this smell.
ImpactMiner: a tool for change impact analysis Dit, Bogdan; Wagner, Michael; Wen, Shasha ...
Companion Proceedings of the 36th International Conference on Software Engineering,
05/2014
Conference Proceeding
Odprti dostop
Developers are often faced with a natural language change request (such as a bug report) and tasked with identifying all code elements that must be modified in order to fulfill the request (e.g., fix ...a bug or implement a new feature). In order to accomplish this task, developers frequently and routinely perform change impact analysis. This formal demonstration paper presents ImpactMiner, a tool that implements an integrated approach to software change impact analysis. The proposed approach estimates an impact set using an adaptive combination of static textual analysis, dynamic execution tracing, and mining software repositories techniques. ImpactMiner is available from our online appendix http://www.cs.wm.edu/semeru/ImpactMiner/
Mining Version Histories for Detecting Code Smells Palomba, Fabio; Bavota, Gabriele; Di Penta, Massimiliano ...
IEEE transactions on software engineering,
2015-May-1, 2015-5-1, 20150501, Letnik:
41, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase changeand fault-proneness. While most of the detection techniques just ...rely on structural information, many code smells are intrinsically characterized by how code elements change overtime. In this paper, we propose Historical Information for Smell deTection (HIST), an approach exploiting change history information to detect instances of five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy. We evaluate HIST in two empirical studies. The first, conducted on 20 open source projects, aimed at assessing the accuracy of HIST in detecting instances of the code smells mentioned above. The results indicate that the precision of HIST ranges between 72 and 86 percent, and its recall ranges between 58 and 100 percent. Also, results of the first study indicate that HIST is able to identify code smells that cannot be identified by competitive approaches solely based on code analysis of a single system's snapshot. Then, we conducted a second study aimed at investigating to what extent the code smells detected by HIST (and by competitive code analysis techniques) reflect developers' perception of poor design and implementation choices. We involved 12 developers of four open source projects that recognized more than 75 percent of the code smell instances identified by HIST as actual design/implementation problems.
High cohesion is a desirable property of software as it positively impacts understanding, reuse, and maintenance. Currently proposed measures for cohesion in Object-Oriented (OO) software reflect ...particular interpretations of cohesion and capture different aspects of it. Existing approaches are largely based on using the structural information from the source code, such as attribute references, in methods to measure cohesion. This paper proposes a new measure for the cohesion of classes in OO software systems based on the analysis of the unstructured information embedded in the source code, such as comments and identifiers. The measure, named the Conceptual Cohesion of Classes (C3), is inspired by the mechanisms used to measure textual coherence in cognitive psychology and computational linguistics. This paper presents the principles and the technology that stand behind the C3 measure. A large case study on three open source software systems is presented which compares the new measure with an extensive set of existing metrics and uses them to construct models that predict software faults. The case study shows that the novel measure captures different aspects of class cohesion compared to any of the existing cohesion measures. In addition, combining C3 with existing structural cohesion metrics proves to be a better predictor of faulty classes when compared to different combinations of structural cohesion metrics.
During software maintenance and evolution the internal structure of the software system undergoes continuous changes. These modifications drift the source code away from its original design, thus ...deteriorating its quality, including cohesion and coupling of classes. Several refactoring methods have been proposed to overcome this problem. In this paper we propose a novel technique to identify Move Method refactoring opportunities and remove the Feature Envy bad smell from source code. Our approach, coined as Methodbook, is based on relational topic models (RTM), a probabilistic technique for representing and modeling topics, documents (in our case methods) and known relationships among these. Methodbook uses RTM to analyze both structural and textual information gleaned from software to better support move method refactoring. We evaluated Methodbook in two case studies. The first study has been executed on six software systems to analyze if the move method operations suggested by Methodbook help to improve the design quality of the systems as captured by quality metrics. The second study has been conducted with eighty developers that evaluated the refactoring recommendations produced by Methodbook. The achieved results indicate that Methodbook provides accurate and meaningful recommendations for move method refactoring operations.
The paper presents an approach that combines conceptual and evolutionary techniques to support change impact analysis in source code. Conceptual couplings capture the extent to which domain concepts ...and software artifacts are related to each other. This information is derived using Information Retrieval based analysis of textual software artifacts that are found in a single version of software (e.g., comments and identifiers in a single snapshot of source code). Evolutionary couplings capture the extent to which software artifacts were co-changed. This information is derived from analyzing patterns, relationships, and relevant information of source code changes mined from multiple versions in software repositories. The premise is that such combined methods provide improvements to the accuracy of impact sets compared to the two individual approaches. A rigorous empirical assessment on the changes of the open source systems Apache httpd, ArgoUML, iBatis, KOffice, and jEdit is also reported. The impact sets are evaluated at the file and method levels of granularity for all the software systems considered in the empirical evaluation. The results show that a combination of conceptual and evolutionary techniques, across several cut-off points and periods of history, provides statistically significant improvements in accuracy over either of the two techniques used independently. Improvements in F-measure values of up to 14% (from 3% to 17%) over the conceptual technique in ArgoUML at the method granularity, and up to 21% over the evolutionary technique in iBatis (from 9% to 30%) at the file granularity were reported.
An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such ...techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pace at which DL techniques have been adopted, it is difficult to distill the current successes, failures, and opportunities of the current research landscape. In an effort to bring clarity to this cross-cutting area of work, from its modern inception to the present, this article presents a systematic literature review of research at the intersection of SE & DL. The review canvasses work appearing in the most prominent SE and DL conferences and journals and spans 128 papers across 23 unique SE tasks. We center our analysis around the components of learning, a set of principles that governs the application of machine learning techniques (ML) to a given problem domain, discussing several aspects of the surveyed work at a granular level. The end result of our analysis is a research roadmap that both delineates the foundations of DL techniques applied to SE research and highlights likely areas of fertile exploration for the future.
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