Hatékony rendszer-szintű hatásanalízis módszerek és alkalmazásuk a szoftverfejlesztés folyamatában = Efficient whole-system impact analysis methods with applications in software development

Szoftver hatásanalízis során a rendszer megváltoztatásának következményeit becsüljük, melynek fontos alkalmazásai vannak például a változtatás-propagálás, költségbecslés, szoftverminőség és tesztelés területén. A kutatás során olyan hatásanalízis módszereket dolgoztunk ki, melyek hatékonyan és sikeresen alkalmazhatók nagyméretű és heterogén architektúrájú, valós alkalmazások esetében is. A korábban rendelkezésre álló módszerek csak korlátozott méretben és környezetekben voltak képesek eredményt szolgáltatni. A meglévő statikus és dinamikus programszeletelés és függőség elemzési algoritmusok továbbfejlesztése mellett számos kapcsolódó területen értünk el eredményeket úgy, mint függőségek metrikákkal történő vizsgálata, fogalmi csatolás kutatása, minőségi modellek, hiba- és produktivitás előrejelzés. Ezen területeknek a módszerek gyakorlatban történő alkalmazásában van jelentősége. Speciális technológiákra koncentrálva újszerű eredmények születtek, például adatbázis rendszerek vagy alacsony szintű nyelvek esetében. A hatásanalízis módszerek alkalmazásai terén kidolgoztunk újszerű módszereket a tesztelés optimalizálása, teszt lefedettség mérés, -priorizálás és változás propagálás területeken. A kidolgozott módszerek alapját képezték további projekteknek, melyek során szoftvertermékeket is kiegészítettek módszereink alapján. | During software change impact analysis, we assess the consequences of changes made to a software system, which has important applications in, for instance, change propagation, cost estimation, software quality and testing. We developed impact analysis methods that can be effectively and efficiently used for large and heterogeneous real life applications as well. Previously available methods could provide results only in limited environments and for systems of limited size. Apart from the enhancements developed for the existing static and dynamic slicing and dependence analysis algorithms, we achieved results in different related areas such as investigation of dependences based on metrics, conceptual coupling, quality models and prediction of defects and productivity. These areas mostly support the application of the methods in practice. We have contributions in the fields of different special technologies, for instance, dependences in database systems or analysis of low level languages. Regarding the applications of impact analysis, we developed novel methods for test optimization, test coverage measurement and prioritization, and change propagation. The developed methods provided basis for further projects, also for extension of certain software products

Genetic improvement of programs

Genetic programming can optimise software, including: evolving test benchmarks, generating hyper-heuristics by searching meta-heuristics, generating communication protocols, composing telephony systems and web services, generating improved hashing and C++ heap managers, redundant programming and even automatic bug fixing. Particularly in embedded real-time or mobile systems, there may be many ways to trade off expenses (such as time, memory, energy, power consumption) vs. Functionality. Human programmers cannot try them all. Also the best multi-objective Pareto trade off may change with time, underlying hardware and network connection or user behaviour. It may be GP can automatically suggest different trade offs for each new market. Recent results include substantial speed up by evolving a new version of a program customised for a special case

Configuring and Assembling Information Retrieval based Solutions for Software Engineering Tasks.

Information Retrieval (IR) approaches are used to leverage textual or unstructured data generated during the software development process to support various software engineering (SE) tasks (e.g., concept location, traceability link recovery, change impact analysis, etc.). Two of the most important steps for applying IR techniques to support SE tasks are preprocessing the corpus and configuring the IR technique, and these steps can significantly influence the outcome and the amount of effort developers have to spend for these maintenance tasks. We present the use of Genetic Algorithms (GAs) to automatically configure and assemble an IR process to support SE tasks. The approach named IR-GA determines the (near) optimal solution to be used for each step of the IR process without requiring any training. We applied IR-GA on three different SE tasks and the results of the study indicate that IR-GA outperforms approaches previously used in the literature, and that it does not significantly differ from an ideal upper bound that could be achieved by a supervised approach and a combinatorial approach

Automatic source code summarization with graph attention networks

Source code summarization aims to generate concise descriptions for code snippets in a natural language, thereby facilitates program comprehension and software maintenance. In this paper, we propose a novel approach–GSCS–to automatically generate summaries for Java methods, which leverages both semantic and structural information of the code snippets. To this end, GSCS utilizes Graph Attention Networks to process the tokenized abstract syntax tree of the program, which employ a multi-head attention mechanism to learn node features in diverse representation sub-spaces, and aggregate features by assigning different weights to its neighbor nodes. GSCS further harnesses an additional RNN-based sequence model to obtain the semantic features and optimizes the structure by combining its output with a transformed embedding layer. We evaluate our approach on two widely-adopted Java datasets; the experiment results confirm that GSCS outperforms the state-of-the-art baselines

Visualization and analysis of software clones

Code clones are identical or similar fragments of code in a software system. Simple copy-paste programming practices of developers, reusing existing code fragments instead of implementing from the scratch, limitations of both programming languages and developers are the primary reasons behind code cloning. Despite the maintenance implications of clones, it is not possible to conclude that cloning is harmful because there are also benefits in using them (e.g. faster and independent development). As a result, researchers at least agree that clones need to be analyzed before aggressively refactoring them. Although a large number of state-of-the-art clone detectors are available today, handling raw clone data is challenging due to the textual nature and large volume. To address this issue, we propose a framework for large-scale clone analysis and develop a maintenance support environment based on the framework called VisCad. To manage the large volume of clone data, VisCad employs the Visual Information Seeking Mantra: overview first, zoom and filter, then provide details-on-demand. With VisCad users can analyze and identify distinctive code clones through a set of visualization techniques, metrics covering different clone relations and data filtering operations. The loosely coupled architecture of VisCad allows users to work with any clone detection tool that reports source-coordinates of the found clones. This yields the opportunity to work with the clone detectors of choice, which is important because each clone detector has its own strengths and weaknesses. In addition, we extend the support for clone evolution analysis, which is important to understand the cause and effect of changes at the clone level during the evolution of a software system. Such information can be used to make software maintenance decisions like when to refactor clones. We propose and implement a set of visualizations that can allow users to analyze the evolution of clones from a coarse grain to a fine grain level. Finally, we use VisCad to extract both spatial and temporal clone data to predict changes to clones in a future release/revision of the software, which can be used to rank clone classes as another means of handling a large volume of clone data. We believe that VisCad makes clone comprehension easier and it can be used as a test-bed to further explore code cloning, necessary in building a successful clone management system

Naming the Identified Feature Implementation Blocks from Software Source Code

Identifying software identifiers that implement a particular feature of a software product is known as feature identification. Feature identification is one of the most critical and popular processes performed by software engineers during software maintenance activity. However, a meaningful name must be assigned to the Identified Feature Implementation Block (IFIB) to complete the feature identification process. The feature naming process remains a challenging task, where the majority of existing approaches manually assign the name of the IFIB. In this paper, the approach called FeatureClouds was proposed, which can be exploited by software developers to name the IFIBs from software code. FeatureClouds approach incorporates word clouds visualization technique to name Feature Blocks (FBs) by using the most frequent words across these blocks. FeatureClouds had evaluated by assessing its added benefit to the current approaches in the literature, where limited tool support was supplied to software developers to distinguish feature names of the IFIBs. For validity, FeatureClouds had applied to draw shapes and ArgoUML software. The findings showed that the proposed approach achieved promising results according to well-known metrics in terms of Precision and Recall