Writing documentation about software internals is rarely considered a rewarding activity. It is highly time-consuming and the resulting documentation is fragile when the software is continuously evolving in a multi-developer setting. Unfortunately, traditional programming environments poorly support the writing and maintenance of documentation. Consequences are severe as the lack of documentation on software structure negatively impacts the overall quality of the software product. We show that using a controlled natural language with a reasoner and a query engine is a viable technique for verifying the consistency and accuracy of documentation and source code. Using ACE, a state-of-the-art controlled natural language, we present positive results on the comprehensibility and the general feasibility of creating and verifying documentation. As a case study, we used automatic documentation verification to identify and fix severe flaws in the architecture of a non-trivial piece of software. Moreover, a user experiment shows that our language is faster and easier to learn and understand than other formal languages for software documentation.
Kimmig al @cite_36 propose an approach for querying source code using a natural language. Users write queries in a simple question that follows the pattern question word - verb - noun - verb ( Where is balance read?'' with balance'' being an instance variable). Our approach differs on two points: (i) With our approach, new concepts and relations are easy to define. Their approach is fairly limited in that respect. (ii) They apply a sophisticated procedure of cleaning and tokenizing the natural language queries, before formalizing them. With our approach, documentation is written in a precise and unambiguous language in the first place. Buse and Weimer @cite_10 synthesize succinct human-readable documentation from software modifications. They employ an approach based on code summarization and symbolic execution, summarizing the runtime conditions necessary for the control flow to reach a modified statement. It is unclear, however, whether such techniques are useful for more than helping to write version log messages.
@cite_36 @cite_10
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Source code modifications are often documented with log messages. Such messages are a key component of software maintenance: they can help developers validate changes, locate and triage defects, and understand modifications. However, this documentation can be burdensome to create and can be incomplete or inaccurate. We present an automatic technique for synthesizing succinct human-readable documentation for arbitrary program differences. Our algorithm is based on a combination of symbolic execution and a novel approach to code summarization. The documentation it produces describes the effect of a change on the runtime behavior of a program, including the conditions under which program behavior changes and what the new behavior is. We compare our documentation to 250 human-written log messages from 5 popular open source projects. Employing a human study, we find that our generated documentation is suitable for supplementing or replacing 89 of existing log messages that directly describe a code change. Natural Language Generation (NLG) is the task of automatically producing natural language text to describe information present in non-linguistic data. It involves three main subtasks: (i) selecting the relevant portion of input data; (ii) determining the words that will be used to verbalise the selected data; and (iii) mapping these words into natural language text. The latter task is known as Surface Realisation (SR). In my thesis, I study the SR task in the context of input data coming from Knowledge Bases (KB). I present two novel approaches to surface realisation from knowledge bases: a supervised approach and a weakly supervised approach. In the first, supervised, approach, I present a corpus-based method for inducing a Feature Based Lexicalized Tree Adjoining Grammar (FB-LTAG) from a parallel corpus of text and data. The resulting grammar includes a unification based semantics and can be used by an existing surface realiser to generate sentences from test data. I show that the induced grammar is compact and generalises well over the test data yielding results that are close to those produced by a handcrafted symbolic approach and which outperform an alternative statistical approach. In the weakly supervised approach, I explore a method for surface realisation from KB data which uses a supplied lexicon but does not require a parallel corpus. Instead, I build a corpus from heterogeneous sources of domain-related text and use it to identify possible lexicalisations of KB symbols (classes and relations) and their verbalisation patterns (frames). Based on the observations made, I build different probabilistic models which are used for selection of appropriate frames and syntax semantics linking while verbalising KB inputs. I evaluate the output sentences and analyse the issues relevant to learning from non-parallel corpora. In both these approaches, I use the data derived from an existing biomedical ontology as a reference input. The proposed methods are generic and can be easily adapted for input from other ontologies for which a parallel non-parallel corpora exists. Current source code analyses driving software maintenance tools treat methods as either a single unit or a set of individual statements or words. They often leverage method names and any existing internal comments. However, internal comments are rare, and method names do not typically capture the method's multiple high-level algorithmic steps that are too small to be a single method, but require more than one statement to implement. Previous work demonstrated feasibility of identifying high level actions automatically for loops; however, many high level actions remain unaddressed and undocumented, particularly sequences of consecutive statements that are associated with each other primarily by object references. We call these object-related action units. In this paper, we present an approach to automatically generate natural language descriptions of object-related action units within methods. We leverage the available, large source of high-quality open source projects to learn the templates of object-related actions, identify the statement that can represent the main action, and generate natural language descriptions for these actions. Our evaluation study of a set of 100 object-related statement sequences showed promise of our approach to automatically identify the action and arguments and generate natural language descriptions. Source code summarization is the task of creating readable summaries that describe the functionality of software. Source code summarization is a critical component of documentation generation, for example as Javadocs formed from short paragraphs attached to each method in a Java program. At present, a majority of source code summarization is manual, in that the paragraphs are written by human experts. However, new automated technologies are becoming feasible. These automated techniques have been shown to be effective in select situations, though a key weakness is that they do not explain the source code's context. That is, they can describe the behavior of a Java method, but not why the method exists or what role it plays in the software. In this paper, we propose a source code summarization technique that writes English descriptions of Java methods by analyzing how those methods are invoked. We then performed two user studies to evaluate our approach. First, we compared our generated summaries to summaries written manually by experts. Then, we compared our summaries to summaries written by a state-of-the-art automatic summarization tool. We found that while our approach does not reach the quality of human-written summaries, we do improve over the state-of-the-art summarization tool in several dimensions by a statistically-significant margin.