Daniel Speicher

Locating the Meaning of Terms in Source Code Research on "Term Introduction"

Software developers are often facing the challenge of understanding a large code base. Program comprehension is not only achieved by looking at object interactions, but also by considering the meaning of the identifiers and the contained terms. Ideally, the source code should exemplify this meaning. We propose to call the source code locations that define the meaning of a term term introduction. We further derive a heuristic to determine the introduction location with the help of an explorative study. This study was performed on 8000 manually evaluated samples gained from 30 open source projects. To support reproducibility, all samples and classifications are also available online. The achieved results show a precision of 75% for the heuristic.

Code Quality Cultivation

Two of the meanings of the word “cultivation” that are rather unrelated show a strong dependency, when applied to the domain of code quality:

Interactive Exploration of Structural Concepts in Code

Understanding a software system is the first task in any reengineering activity. For this very challenging task one effective approach is to identify interesting and reoccuring structures in the software and to study these structures individually. In object-oriented software such structures typically consist of a few classes. The well known among them are called design pattern. Yet, which structures to look at in particular? Can we identify interesting structures that are not that well known? Which structures to be a clue to start with?

SwitchFinder – a novel method and query facility for discovering dynamic gene expression patterns

BackgroundBiological systems and processes are highly dynamic. To gain insights into their functioning time-resolved measurements are necessary. Time-resolved gene expression data captures temporal behaviour of the genes genome-wide under various biological conditions: in response to stimuli, during cell cycle, differentiation or developmental programs. Dissecting dynamic gene expression patterns from this data may shed light on the functioning of the gene regulatory system. The present approach facilitates this discovery. The fundamental idea behind it is the following: there are change-points (switches) in the gene behaviour separating intervals of increasing and decreasing activity, whereas the intervals may have different durations. Elucidating the switch-points is important for the identification of biologically meanigfull features and patterns of the gene dynamics.ResultsWe developed a statistical method, called SwitchFinder, for the analysis of time-series data, in particular gene expression data, based on a change-point model. Fitting the model to the gene expression time-courses indicates switch-points between increasing and decreasing activities of each gene. Two types of the model - based on linear and on generalized logistic function - were used to capture the data between the switch-points. Model inference was facilitated with the Bayesian methodology using Markov chain Monte Carlo (MCMC) technique Gibbs sampling. Further on, we introduced features of the switch-points: growth, decay, spike and cleft, which reflect important dynamic aspects. With this, the gene expression profiles are represented in a qualitative manner - as sets of the dynamic features at their onset-times. We developed a Web application of the approach, enabling to put queries to the gene expression time-courses and to deduce groups of genes with common dynamic patterns.SwitchFinder was applied to our original data - the gene expression time-series measured in neuroblastoma cell line upon treatment with all-trans retinoic acid (ATRA). The analysis revealed eight patterns of the gene expression responses to ATRA, indicating the induction of the BMP, WNT, Notch, FGF and NTRK-receptor signaling pathways involved in cell differentiation, as well as the repression of the cell-cycle related genes.ConclusionsSwitchFinder is a novel approach to the analysis of biological time-series data, supporting inference and interactive exploration of its inherent dynamic patterns, hence facilitating biological discovery process. SwitchFinder is freely available at https://newbioinformatics.eu/switchfinder.

Code Museums as Functional Tests for Static Analyses

Growing your software guided by tests [2] has the benefit of thoroughly tested implementations of the right functionality. If you are developing static analyses your “test data” consist of code that has a few lines to a few classes. How can tests based on this “data” be kept expressive and maintainable? After exploring a variety of different other approaches we decided to embed the expectations into the data instead of embedding the data into test cases. Figure 1 presents an overview of our tool set. The functional tests are within Java code, the tested static analyses are implemented as logic programs.