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Proceedings of the Third Italian Conference on Computational Linguistics CLiC-it 2016

 | 
Anna Corazza
, 
Simonetta Montemagni
, 
Giovanni Semeraro

A New Dataset for Source Code Comment Coherence

Anna Corazza, Valerio Maggio et Giuseppe Scanniello

Résumé

Source code comments provide useful insights on a codebase and on the intent behind design decisions and goals. Often, the information provided in the comment of a method and in its corresponding implementation may be not coherent with each other (i.e., the comment does not properly describe the implementation). Several could be the motivations for this issue (e.g., comment and source code do not evolve coherently). In this paper, we present the results of a manual assessment on the coherence between comments and implementations of 3, 636 methods, gathered from 4 Java open-source software. The results of this assessment has been collected in a dataset that we made publicly available on the web. We also sketch here the protocol to create this dataset.

I commenti al codice sorgente forniscono informazioni utili sull’implementazione del codice e sulle intenzioni relative alle decisioni e agli scopi del progetto. Spesso, le informazioni presenti nel commento di un metodo e nella sua implementazione possono non essere coerenti (nel senso che il commento non da` una descrizione adeguata dell’implementazione). Ci possono essere diverse spiegazioni per questo (ad esempio, commenti e codice sorgente non sono stati modificati in modo coerente). In questo articolo, presentiamo i risultati di una valutazione manuale della coerenza tra commenti e implementazione di 3, 636 metodi, raccolti da 4 applicazioni open source in Java. I risultati di questa valutazione sono stati raccolti in un dataset che abbiamo pubblicato sul web. Accenniamo anche al protocollo seguito per la preparazione del dataset.

Texte intégral

1 Introduction

1Natural language is used in different ways in the development process of a software system and therefore techniques of natural language processing (NLP) and information retrieval (IR) are more and more frequently integrated into software development and maintenance tools. Many automated or semi-automated techniques have been proposed to aid developers in the comprehension and in the evolution of existing systems, e.g., (Corazza et al., 2016; Scanniello et al., 2010). Natural language information is provided in source code comments and in the name of identifiers. In the former case, standard natural language is usually adopted, although quite technical. Comments are written in English, even when developers have different mother-tongues. On the other hand, identifiers are typically constructed by composing multiple terms and abbreviations. Therefore, more sophisticated techniques are necessary to extract the lexical information contained in each identifier (Corazza et al., 2012).

2Most of these techniques assume that the same words are used whenever referring to a particular concept (Lawrie et al., 2010). In many cases, this represents an oversimplification: methods are often modified without updating the corresponding comments (Salviulo and Scanniello, 2014). In these cases, comments might convey information unrelated or inconsistent with the corresponding implementation. Nevertheless, comments are extremely important because they are expected to convey the main intent behind design decisions, along with some implementation details (e.g., types of parameters and of returned values).

3Therefore, more sophisticated models are necessary to determine if there is coherence between the lexicon provided in comments and in its corresponding source code. Hence, there exists coherence between a lead comment of a method and its source code (also simply coherence, from here on) if that comment describes the intent of the method and its actual implementation.

4In this work, we focus on the lead comment of methods. This kind of comments precedes the definition of a given method and is supposed to provide its documentation and details about the implementation. We discuss here a dataset we made publicly available on the web1. It contains annotations about the coherence of 3, 636 methods collected from 4 implementations of 3 open source projects written in Java. The defined protocol used for its creation is also sketched, to give researchers the opportunity to possibly extend it. Further details on this protocol can be found in (Corazza et al., 2015).

5For the assessment of the quality of the annotation with special focus on computational linguistics applications, a few indexes have been considered (Eugenio and Glass, 2004; Artstein and Poesio, 2008; Mathet et al., 2015), among which the kappa index (Cohen, 1960) is the most widely adopted because of its favorable characteristics. The inter-annotator agreement has therefore been assessed by this parameter.

6We expect that making freely available this dataset could give impulse to the research for approaches to assess the coherence between the implementation of a method and its lead comment (simply coherence, from here on). In fact, although no approach has been yet proposed in this regard, they could be of great help for software maintenance and evolution activities.

7The paper is structured as follows. In Section 2, we discuss the methodology used to create our dataset. A description of the main characteristics of the dataset is given in Section 3, while in Section 4 the annotation is assessed. Some final considerations conclude the paper.

2 Dataset Construction

8To create our dataset, we adopted the perspective-based and the checklist-based review methods (Wohlin et al., 2012). The perspective is the one of the Researcher aiming at assessing the coherence between the lead comment of a method and its implementation. The process of creation is based on the following elements:

  1. Working Meetings. We used meetings to determine the goals of our research work and the process to create the dataset.

  2. Dataset Creation. We instantiated the defined process to create our dataset.

  3. Outcomes. We gathered results during and after the creation of our dataset.

  4. Publishing Results and Dataset. We shared our experience with the community in (Corazza et al., 2015) and released the dataset on the web.

9The construction of the dataset has been completed in two main consecutive phases by using an ad-hoc web system implemented for the purpose:

  • Verify coherence. Annotators verify by means of a checklist the coherence between the lead comments of a set of methods and their corresponding implementation.

  • Resolve conflicts. The intervention of experts is required whenever the judgements of the annotators differ. In our case, two of the authors, with a background in software engineering, assumed the role of experts and examined the problematic cases. For each conflicting method, the experts should reach an agreement about the coherence or the non-coherence. Methods on which experts do not get a consensus are automatically discarded.

3 Dataset Description

10Some descriptive statistics (e.g., number of classes and methods) of the software systems in our dataset are shown in Table 1.

  • CoffeeMaker2 is a software to manage inventory and recipes and to purchase beverages. We chose this software because it has a simple and clear design (being it developed for educational purposes).

  • JFreechart3 is a Java tool supporting the visualization of data charts (e.g., scatter plots and histograms). We included two versions of this software. As reported in Table 1, both these versions contain almost the 80% of methods with lead comments. This suggests an extensive use of comments, which is the main reason why we decided to include this software in the dataset.

  • JHotDraw4 is a framework for technical and structured graphics. Even if the source code of JHotDraw is scarcely commented (see Table 1), it is well-known in the software maintenance community due to its good Object-Oriented design (Scanniello et al., 2010).

Table 1: Descriptive statistics of the software systems in the dataset: Nf stands for the number of files, Nc for the number of classes, Nm for the number of methods and Nm* for the number of methods with lead comments.

System

Version

Nf

Nc

Nm

Nm*

CoffeeMaker

-

7

7

51

47 (92%)

JFreeChart 6.0

0.6.0

82

83

617

485 (79%)

JFreeChart 7.1

0.7.1

124

127

807

624 (77%)

JHotDraw

7.4.1

575

692

6414

2480 (39%)

11In Figure 1, we report the implementation and the lead comment of setTickLabelsVisible (extracted from JFreeChart ver. 0.7.1, included in our dataset). According to our definition of coherence, we can assert that this method is coherent. On the other hand, the save method reported in Figure 2 provides a very poor and inadequate description of the design intent of the method, thus reflecting a lack of coherence with the underlying implementation.

12Three annotators were involved in the dataset creation process. Two of them hold a Bachelor degree in Computer Science, and have very similar technical backgrounds. On the other hand, the third annotator can be considered more experienced than the other two since he holds a Master degree in Computer Science. We distributed the effort among the annotators so that each software would be separately evaluated by at least two annotators. This allowed us to have multiple judgements for each method in the dataset, and to calculate the rate of agreement among annotators.

Figure 1: The lead comment and the implementation of the method setTickLabelsVisible of JFreeChart 0.7.1

Image 10000000000003BA000001DC51D2D82F.jpg

Figure 2: Non-Coherent method in JHotDraw 7.4.1

Image 10000000000003BE000002A48CD06A41.jpg

4 Annotation Assessment

13The whole dataset creation process occurred from January, 15th 2014 to June, 20th 2014, for a total of 800 man-hours. This gives an estimation of the effort required to conduct the study presented in this paper, and provides an indication to the researcher interested in extending our dataset.

14The annotators provided indications on the coherence of methods by assigning them one out of three following possible values: NonCoherent, Don’t Know, and Coherent.

15In this scenario, we use the kappa index (Cohen, 1960) to obtain an assessment of the agreement among annotators, thus estimating the reliability of their evaluations. In fact, if annotators agree on a large number of methods, we can conclude that their annotations are reliable. The kappa index is designed for categorical judgments and refers the agreement rate calculation to the rate of chance agreement:

Image 10000000000000CC000000232311E0B4.jpg

16po is the observed probability of agreement, while pc is the chance probability of agreement. Both probabilities are estimated by the corresponding frequencies. By a simple algebraic manipulation, Equation 1 can be written as:

Image 10000000000000D2000000238E70B9A5.jpg

  • 5 The notation means that 1 is included in the interval, while 0 is not.

17where qo = 1 po and qc = 1 pc and correspond to the observed and the chance probabilities of disagreement, respectively. Usually the index assumes values in ]0, 1]5, as it can be expected that the observed disagreement is less likely than chance. A null value signals that observed disagreement is exactly as likely as chance, while the kappa index assumes negative values in the unwanted case where disagreement is more likely than chance. Perfect agreement corresponds to k = 1. Values greater than 0.80 are usually considered as a cue of good agreement. Values in the interval [0.67, 0.80] are considered acceptable (Cohen, 1960).

18The classical formulation of the kappa index considers a binary classification problem (e.g., Non-Coherent or Coherent). However in our case, the neutral judgement (i.e., Don’t know) is also allowed. Therefore, possible disagreements include the case where one of the two answers is the neutral one. In this case, it is possible to differently weigh the possible disagreements among annotators. In fact, disagreements due to the neutral answers are less serious than disagreements where judgments are totally divergent (i.e., Coherent and Non-Coherent, in our case). To this end, Cohen (Cohen, 1968) presents a variant of the kappa index, where in case of a disagreement, different weights can be applied. In case the same weight is assigned to all possible disagreement combinations, the original (unweighted) formulation is obtained. The formulation of the Weighted Kappa (WK) is the one in the equation (2), but for the computation of qo and qc the contributions are weighted according to the importance given to the corresponding disagreement cases. By contrast, we refer to the original formulation of the kappa index as Unweighted Kappa (UK). We assign to the Don’t know response a weight that is half the weight assigned to the Not-Coherent (or Coherent) one. This is the same schema reported by Cohen (Cohen, 1968). Weighted and Unweighted kappa indexes are reported in Table 2.

Table 2: Agreement Rate of Judges as computed by the Cohen’s Kappa Index.

System

UK

WK

CoffeeMaker

0.913

0.913

JFreeChart 6.0

0.939

0.918

JFreeChart 7.1

0.983

0.977

JHotDraw

0.824

0.684

19The agreement between annotators is good on the first three systems, and acceptable for JHotDraw. However, on this system the difference between the values for UK and WK is large, thus providing a more accurate indication on the agreement of the evaluations on this software.

20At the end of the first step of the dataset creation process, the number of methods on which annotators did not agree was 302, corresponding to the 8.3% of the total number of methods from all the systems in the dataset. Most of these methods are those in JHotDraw, as suggested by the kappa index values (see Table 2). These methods were reviewed by two of the authors. An agreement was reached on all of these methods, which were then included in the dataset. The total number of methods in the dataset is reported in Table 3 (i.e., 2, 883).

5 Conclusions and future work

21In this paper, we have presented the early steps of our research on the coherence between the lead comment of methods and their implementations. In particular, we have provided a description of the problem settings, along with the experimental protocol defined to create our dataset. We made it publicly available on the web. We also sketched the results of quantitative analysis conducted on a codebase of 3, 636 methods, gathered from 4 different open-source systems written in Java.

22There could be many possible future directions for our research. For example, it would be interesting to conduct an empirical study to investigate the effect of maintenance operations on the coherence for multiple versions of the same system. As a step forward in this future research direction, we already included in the dataset two versions of the JFreeChart system. Our results and those by Fluri et al. (Fluri et al., 2007) represent a viable starting point. Finally, we would like to exploit the collected data as an evaluation set to assess the performance of approaches able to discern method coherence.

Table 3: Descriptive Statistics of the Dataset: C stands for Coherent, NC for Non-Coherent.

System

C

NC

Total

Don’t Know
(Not included)

CoffeeMaker

27

20

47

0

JFreeChart 6.0

406

55

461

24

JFreeChart 7.1

520

68

588

36

JHotDraw

762

1025

1787

693

Total

1715

2883

Bibliographie

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Auteurs

DIETI, Univ. di Napoli Federico II - anna.corazza@unina.it

Fondazione Bruno Kessler - vmaggio@fbk.eu

Dept. of Mathematics, Information Technology, and Economics, Univ. della Basilicata - giuseppe.scanniello@unibas.it

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