Themistoklis Diamantopoulos and Andreas Symeonidis, "Enhancing Requirements Reusability through Semantic Modeling and Data Mining Techniques", in Enterprise Information Systems,
| Bibtex Enhancing the requirements elicitation process has always been of added value to software engineers, since it expedites the software lifecycle and reduces errors in the conceptualization phase of software products. The challenge posed to the research community is to construct formal models that are capable of storing requirements from multimodal formats (text and UML diagrams) and promote easy requirements reuse, while at the same time being traceable to allow full control of the system design, as well as comprehensible to software engineers and end users. In this work, we present an approach that enhances requirements reuse while capturing the static (functional requirements, use case diagrams) and dynamic (activity diagrams) view of software projects. Our ontology-based approach allows for reasoning over the stored requirements, while the mining methodologies employed detect incomplete or missing software requirements, this way reducing the effort required for requirements elicitation at an early stage of the project lifecycle.
Themistoklis Diamantopoulos, Michael Roth, Andreas Symeonidis and Ewan Klein, "Software Requirements as an Application Domain for Natural Language Processing", in Language Resources and Evaluation,
vol 51, no 2, pp. 495-524,
| Bibtex | Preprint Mapping functional requirements first to specifications and then to code is one of the most challenging tasks in software development. Since requirements are commonly written in natural language, they can be prone to ambiguity, incompleteness and inconsistency. Structured semantic representations allow requirements to be translated to formal models, which can be used to detect problems at an early stage of the development process through validation. Storing and querying such models can also facilitate software reuse. Several approaches constrain the input format of requirements to produce specifications, however they usually require considerable human effort in order to adopt domain-specific heuristics and/or controlled languages. We propose a mechanism that automates the mapping of requirements to formal representations using semantic role labeling. We describe the first publicly available dataset for this task, employ a hierarchical framework that allows requirements concepts to be annotated, and discuss how semantic role labeling can be adapted for parsing software requirements.
Christoforos Zolotas, Themistoklis Diamantopoulos, Kyriakos Chatzidimitriou and Andreas Symeonidis, "From requirements to source code: a Model-Driven Engineering approach for RESTful web services", in Automated Software Engineering,
vol 24, no 4, pp. 791-838,
| Bibtex | Preprint During the last few years, the REST architectural style has drastically changed the way web services are developed. Due to its transparent resource-oriented model, the RESTful paradigm has been incorporated into several development frameworks that allow rapid development and aspire to automate parts of the development process. However, most of the frameworks lack automation of essential web service functionality, such as authentication or database searching, while the end product is usually not fully compliant to REST. Furthermore, most frameworks rely heavily on domain specific modeling and require developers to be familiar with the employed modeling technologies. In this paper, we present a Model-Driven Engineering (MDE) engine that supports fast design and implementation of web services with advanced functionality. Our engine provides a front-end interface that allows developers to design their envisioned system through software requirements in multimodal formats. Input in the form of textual requirements and graphical storyboards is analyzed using natural language processing techniques and semantics, to semi-automatically construct the input model for the MDE engine. The engine subsequently applies model-to-model transformations to produce a RESTful, ready-to-deploy web service. The procedure is traceable, ensuring that changes in software requirements propagate to the underlying software artefacts and models. Upon assessing our methodology through a case study and measuring the effort reduction of using our tools, we conclude that our system can be effective for the fast design and implementation of web services, while it allows easy wrapping of services that have been engineered with traditional methods to the MDE realm.
Themistoklis Diamantopoulos and Andreas Symeonidis, "Localizing Software Bugs using the Edit Distance of Call Traces", in International Journal on Advances in Software,
vol 7, no 1, pp. 277-288,
| Bibtex | Preprint Automating the localization of software bugs that do not lead to crashes is a difficult task that has drawn the attention of several researchers. Several popular methods follow the same approach; function call traces are collected and represented as graphs, which are subsequently mined using subgraph mining algorithms in order to provide a ranking of potentially buggy functions-nodes. Recent work has indicated that the scalability of state-of-the-art methods can be improved by reducing the graph dataset using tree edit distance algorithms. The call traces that are closer to each other, but belong to different sets, are the ones that are most significant in localizing bugs. In this work, we further explore the task of selecting the most significant traces, by proposing different call trace selection techniques, based on the Stable Marriage problem, and testing their effectiveness against current solutions. Upon evaluating our methods on a real-world dataset, we prove that our methodology is scalable and effective enough to be applied on dynamic bug detection scenarios.
Valasia Dimaridou, Alexandros-Charalampos Kyprianidis, Michail Papamichail, Themistoklis Diamantopoulos and Andreas L. Symeonidis, "Assessing the User-Perceived Quality of Source Code Components using Static Analysis Metrics", in Communications in Computer and Information Science (CCIS),
vol 868, pp. 3-27,
| Bibtex Nowadays, developers tend to adopt a component-based software engineering approach, reusing own implementations and/or resorting to third-party source code. This practice is in principle cost-effective, however it may also lead to low quality software products, if the components to be reused exhibit low quality. Thus, several approaches have been developed to measure the quality of software components. Most of them, however, rely on the aid of experts for defining target quality scores and deriving metric thresholds, leading to results that are context-dependent and subjective. In this work, we build a mechanism that employs static analysis metrics extracted from GitHub projects and defines a target quality score based on repositories’ stars and forks, which indicate their adoption/acceptance by developers. Upon removing outliers with a one-class classifier, we employ Principal Feature Analysis and examine the semantics among metrics to provide an analysis on five axes for source code components (classes or packages): complexity, coupling, size, degree of inheritance, and quality of documentation. Neural networks are thus applied to estimate the final quality score given metrics from these axes. Preliminary evaluation indicates that our approach effectively estimates software quality at both class and package levels.
Kyriakos C. Chatzidimitriou, Michail Papamichail, Themistoklis Diamantopoulos, Michail Tsapanos, and Andreas L. Symeonidis, "npm-miner: An Infrastructure for Measuring the Quality of the npm Registry", in IEEE/ACM 15th International Conference on Mining Software Repositories (MSR),
Gothenburg, Sweden, May 2018.
Themistoklis Diamantopoulos, Georgios Karagiannopoulos, and Andreas Symeonidis, "CodeCatch: Extracting Source Code Snippets from Online Sources", in IEEE/ACM 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE),
Gothenburg, Sweden, May 2018.
| Bibtex | Preprint Nowadays, developers rely on online sources to find example snippets that address the programming problems they are trying to solve. However, contemporary API usage mining methods are not suitable for locating easily reusable snippets, as they provide usage examples for specific APIs, thus requiring the developer to know which library to use beforehand. On the other hand, the approaches that retrieve snippets from online sources usually output a list of examples, without aiding the developer to distinguish among different implementations and without offering any insight on the quality and the reusability of the proposed snippets. In this work, we present CodeCatch, a system that receives queries in natural language and extracts snippets from multiple online sources. The snippets are assessed both for their quality and for their usefulness/preference by the developers, while they are also clustered according to their API calls to allow the developer to select among the different implementations. Preliminary evaluation of CodeCatch in a set of indicative programming problems indicates that it can be a useful tool for the developer.
Nikolaos Katirtzis, Themistoklis Diamantopoulos, and Charles Sutton, "Summarizing Software API Usage Examples using Clustering Techniques", in 21th International Conference on Fundamental Approaches to Software Engineering (FASE),
Thessaloniki, Greece, April 2018.
| Bibtex | Preprint | Code | Dataset As developers often use third-party libraries to facilitate software development, the lack of proper API documentation for these libraries undermines their reuse potential. And although several approaches extract usage examples for libraries, they are usually tied to specific language implementations, while their produced examples are often redundant and are not presented as concise and readable snippets. In this work, we propose a novel approach that extracts API call sequences from client source code and clusters them to produce a diverse set of source code snippets that effectively covers the target API. We further construct a summarization algorithm to present concise and readable snippets to the users. Upon evaluating our system on software libraries, we indicate that it achieves high coverage in API methods, while the produced snippets are of high quality and closely match handwritten examples.
Michail Papamichail, Themistoklis Diamantopoulos, Ilias Chrysovergis, Philippos Samlidis and Andreas Symeonidis, "User-Perceived Reusability Estimation based on Analysis of Software Repositories", in 2018 IEEE International Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE),
Campobasso, Italy, March 2018.
| Bibtex The popularity of open-source software repositories has led to a new reuse paradigm, where online resources can be thoroughly analyzed to identify reusable software components. Obviously, assessing the quality and specifically the reusability potential of source code residing in open software repositories poses a major challenge for the research community. Although several systems have been designed towards this direction, most of them do not focus on reusability. In this paper, we define and formulate a reusability score by employing information from GitHub stars and forks, which indicate the extent to which software components are adopted/accepted by developers. Our methodology involves applying and assessing different state-of-the-practice machine learning algorithms, in order to construct models for reusability estimation at both class and package levels. Preliminary evaluation of our methodology indicates that our approach can successfully assess reusability, as perceived by developers.
Valasia Dimaridou, Alexandros-Charalampos Kyprianidis, Michail Papamichail, Themistoklis Diamantopoulos and Andreas L. Symeonidis, "Towards Modeling the User-perceived Quality of Source Code using Static Analysis Metrics", in 12th International Conference on Software Technologies (ICSOFT),
Madrid, Spain, July 2017.
| Bibtex | Preprint Nowadays, software has to be designed and developed as fast as possible, while maintaining quality standards. In this context, developers tend to adopt a component-based software engineering approach, reusing own implementations and/or resorting to third-party source code. This practice is in principle cost-effective, however it may lead to low quality software products. Thus, measuring the quality of software components is of vital importance. Several approaches that use code metrics rely on the aid of experts for defining target quality scores and deriving metric thresholds, leading to results that are highly context-dependent and subjective. In this work, we build a mechanism that employs static analysis metrics extracted from GitHub projects and defines a target quality score based on repositories’ stars and forks, which indicate their adoption/acceptance by the developers’ community. Upon removing outliers with a one-class classifier, we employ Principal Feature Analysis and exam ine the semantics among metrics to provide an analysis on five axes for a source code component: complexity, coupling, size, degree of inheritance, and quality of documentation. Neural networks are used to estimate the final quality score given metrics from all of these axes. Preliminary evaluation indicates that our approach can effectively estimate software quality.
Michail Papamichail, Themistoklis Diamantopoulos and Andreas L. Symeonidis, "User-Perceived Source Code Quality Estimation based on Static Analysis Metrics", in 2016 IEEE International Conference on Software Quality, Reliability and Security (QRS),
Vienna, Austria, August 2016.
| Bibtex | Preprint The popularity of open source software repositories and the highly adopted paradigm of software reuse have led to the development of several tools that aspire to assess the quality of source code. However, most software quality estimation tools, even the ones using adaptable models, depend on fixed metric thresholds for defining the ground truth. In this work we argue that the popularity of software components, as perceived by developers, can be considered as an indicator of software quality. We present a generic methodology that relates quality with source code metrics and estimates the quality of software components residing in popular GitHub repositories. Our methodology employs two models: a one-class classifier, used to rule out low quality code, and a neural network, that computes a quality score for each software component. Preliminary evaluation indicates that our approach can be effective for identifying high quality software components in the context of reuse.
Themistoklis Diamantopoulos and Antonis Noutsos and Andreas L. Symeonidis, "DP-CORE: A Design Pattern Detection Tool for Code Reuse", in 6th International Symposium on Business Modeling and Software Design (BMSD),
Rhodes, Greece, June 2016.
| Bibtex | Preprint | Code In order to maintain, extend or reuse software projects one has to primarily understand what a system does and how well it does it. And, while in some cases information on system functionality exists, information covering the non-functional aspects is usually unavailable. Thus, one has to infer such knowledge by extracting design patterns directly from the source code. Several tools have been developed to identify design patterns, however most of them are limited to compilable and in most cases executable code, they rely on complex representations, and do not offer the developer any control over the detected patterns. In this paper we present DP-CORE, a design pattern detection tool that defines a highly descriptive representation to detect known and define custom patterns. DP-CORE is flexible, identifying exact and approximate pattern versions even in non-compilable code. Our analysis indicates that DP-CORE provides an efficient alternative to existing design pattern detection tools.
Themistoklis Diamantopoulos, Klearchos Thomopoulos, and Andreas Symeonidis, "QualBoa: reusability-aware recommendations of source code components", in IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR),
Austin, Texas, USA, May 2016.
| Bibtex | Preprint | Code | Dataset Contemporary software development processes involve finding reusable software components from online repositories and integrating them to the source code, both to reduce development time and to ensure that the final software project is of high quality. Although several systems have been designed to automate this procedure by recommending components that cover the desired functionality, the reusability of these components is usually not assessed by these systems. In this work, we present QualBoa, a recommendation system for source code components that covers both the functional and the quality aspects of software component reuse. Upon retrieving components, QualBoa provides a ranking that involves not only functional matching to the query, but also a reusability score based on configurable thresholds of source code metrics. The evaluation of QualBoa indicates that it can be effective for recommending reusable source code.
Themistoklis Diamantopoulos and Andreas Symeonidis, "Towards Interpretable Defect-Prone Component Analysis using Genetic Fuzzy Systems", in IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE),
Florence, Italy, May 2015.
| Bibtex | Preprint The problem of Software Reliability Prediction is attracting the attention of several researchers during the last few years. Various classification techniques are proposed in current literature which involve the use of metrics drawn from version control systems in order to classify software components as defect-prone or defect-free. In this paper, we create a novel genetic fuzzy rule-based system to efficiently model the defect-proneness of each component. The system uses a Mamdani-Assilian inference engine and models the problem as a one-class classification task. System rules are constructed using a genetic algorithm, where each chromosome represents a rule base (Pittsburgh approach). The parameters of our fuzzy system and the operators of the genetic algorithm are designed with regard to producing interpretable output. Thus, the output offers not only effective classification, but also a comprehensive set of rules that can be easily visualized to extract useful conclusions about the metrics of the software.
Themistoklis Diamantopoulos and Andreas Symeonidis, "Employing Source Code Information to Improve Question-answering in Stack Overflow", in IEEE/ACM 12th Working Conference on Mining Software Repositories (MSR),
Florence, Italy, May 2015.
| Bibtex | Preprint Nowadays, software development has been greatly influenced by question-answering communities, such as Stack Overflow. A new problem-solving paradigm has emerged, as developers post problems they encounter that are then answered by the community. In this paper, we propose a methodology that allows searching for solutions in Stack Overflow, using the main elements of a question post, including not only its title, tags, and body, but also its source code snippets. We describe a similarity scheme for these elements and demonstrate how structural information can be extracted from source code snippets and compared to further improve the retrieval of questions. The results of our evaluation indicate that our methodology is effective on recommending similar question posts allowing community members to search without fully forming a question.
Michael Roth, Themistoklis Diamantopoulos, Ewan Klein and Andreas L. Symeonidis, "Software Requirements: A new Domain for Semantic Parsers", in ACL 2014 Workshop on Semantic Parsing (SP14),
Baltimore, Maryland, USA, June 2014.
| Bibtex | Preprint Software requirements are commonly written in natural language, making them prone to ambiguity, incompleteness and inconsistency. By converting requirements to formal emantic representations, emerging problems can be detected at an early stage of the development process, thus reducing the number of ensuing errors and the development costs. In this paper, we treat the mapping from requirements to formal representations as a semantic parsing task. We describe a novel data set for this task that involves two contributions: first, we establish an ontology for formally representing requirements; and second, we introduce an iterative annotation scheme, in which formal representations are derived through step-wise refinements.
Themistoklis Diamantopoulos, Dionysios Kehagias, Felix G. König and Dimitrios Tzovaras, "Use of density-based cluster analysis and classification techniques for traffic congestion prediction and visualisation", in Transport Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment,
Paris, France, April 2014.
| Bibtex | Preprint The field of Intelligent Transportation Systems has lately raised increasing interest due to its high socio-economic impact. This work aims on developing efficient techniques for traffic congestion prediction and visualisation. We have designed a simple, yet effective and scalable model to handle sparse data from GPS observations and reduce the problem of congestion prediction to a binary classification problem (jam, non-jam). An attempt to generalise the problem is performed by exploring the impact of discriminative versus generative classifiers when employed to produce results in a 30-minute interval ahead of present time. In addition, we present a novel congestion prediction algorithm based on using correlation metrics to improve feature selection. Concerning the visualisation of traffic jams, we present a traffic jam visualisation approach based on cluster analysis that identifies dense congestion areas.
Themistoklis Diamantopoulos and Andreas Symeonidis, "Towards Scalable Bug Localization using the Edit Distance of Call Traces", in Eighth International Conference on Software Engineering Advances (ICSEA),
Venice, Italy, October 2013.
| Bibtex | Preprint Locating software bugs is a difficult task, especially if they do not lead to crashes. Current research on automating non-crashing bug detection dictates collecting function call traces and representing them as graphs, and reducing the graphs before applying a subgraph mining algorithm. A ranking of potentially buggy functions is derived using frequency statistics for each node (function) in the correct and incorrect set of traces. Although most existing techniques are effective, they do not achieve scalability. To address this issue, this paper suggests reducing the graph dataset in order to isolate the graphs that are significant in localizing bugs. To this end, we propose the use of tree edit distance algorithms to identify the traces that are closer to each other, while belonging to different sets. The scalability of two proposed algorithms, an exact and a faster approximate one, is evaluated using a dataset derived from a real-world application. Finally, although the main scope of this work lies in scalability, the results indicate that there is no compromise in effectiveness.
Themistoklis Diamantopoulos, Dionysios Kehagias, Felix G. König and Dimitrios Tzovaras, "Investigating the effect of global metrics in travel time forecasting", in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC),
Hague, Netherlands, October 2013.
| Bibtex The effect of traffic in routing, either for individuals or fleets, becomes more and more noticeable as the social, economical, and the ecological effects that it has, seem to be crucial. Forecasting travel times is an interesting, yet challenging problem, which if taken into careful consideration, could have a positive impact on the effectiveness of Intelligent Transportation Systems. Upon analyzing the problem and describing its variances, this paper compares different methodologies on traffic prediction, along with analyzing the effect of metrics, such as Principal Component Analysis and Cross Correlation, when interpreting traffic data. We evaluate known literature methods along with a new prototype algorithmic variation of STARIMA, based on the use of global Coefficient of Determination, against two diverse datasets. The benchmarking results, which are promising, are discussed with respect to the distinct characteristics of the two datasets.
Themistoklis Diamantopoulos, Andreas Symeonidis and Anthony Chrysopoulos, "Designing Robust Strategies for Continuous Trading in Contemporary Power Markets", in Joint Workshop on Trading Agent Design and Analysis (TADA) and Agent-Mediated Electronic Commerce (AMEC),
Valencia, Spain, June 2012.
| Bibtex | Preprint In contemporary energy markets participants interact with each other via brokers that are responsible for the proper energy flow to and from their clients (usually in the form of long-term or short-term contracts). Power TAC is a realistic simulation of a real-life energy market, aiming towards providing a better understanding and modeling of modern energy markets, while boosting research on innovative trading strategies. Power TAC models brokers as software agents, competing against each other in Double Auction environments, in order to increase their client base and market share. Current work discusses such a broker agent architecture, striving to maximize his own profit. Within the context of our analysis, Double Auction markets are treated as microeconomic systems and, based on state-of-the-art price formation strategies, the following policies are designed: an adaptive price formation policy, a policy for forecasting energy consumption that employs Time Series Analysis primitives, and two shout update policies, a rule-based policy that acts rather hastily, and one based on Fuzzy Logic. The results are quite encouraging and will certainly call for future research.